[Analysis of influencing factors of snow hyperspectral polarized reflections].

Science.gov (United States)

Sun, Zhong-Qiu; Zhao, Yun-Sheng; Yan, Guo-Qian; Ning, Yan-Ling; Zhong, Gui-Xin

2010-02-01

Due to the need of snow monitoring and the impact of the global change on the snow, on the basis of the traditional research on snow, starting from the perspective of multi-angle polarized reflectance, we analyzed the influencing factors of snow from the incidence zenith angles, the detection zenith angles, the detection azimuth angles, polarized angles, the density of snow, the degree of pollution, and the background of the undersurface. It was found that these factors affected the spectral reflectance values of the snow, and the effect of some factors on the polarization hyperspectral reflectance observation is more evident than in the vertical observation. Among these influencing factors, the pollution of snow leads to an obvious change in the snow reflectance spectrum curve, while other factors have little effect on the shape of the snow reflectance spectrum curve and mainly impact the reflection ratio of the snow. Snow reflectance polarization information has not only important theoretical significance, but also wide application prospect, and provides new ideas and methods for the quantitative research on snow using the remote sensing technology.

Study on Development of Non-Destructive Measurement Technique for Viability of Lettuce Seed (Lactuca sativa L) Using Hyperspectral Reflectance Imaging

Energy Technology Data Exchange (ETDEWEB)

Ahn, Chi Kook; Cho, Byoung Kwan [College of Agriculture and Life Science, Chungnam National University, Daejeon (Korea, Republic of); Mo, Chang Yeon [National Acadamy of Agricultural Science, Daejeon (Korea, Republic of); Kim, Moon S. [Environmental Microbial and Food Safety Laboratory, Animal and Natural Resources Institute, Agricultural Research Service, United States Department of Agriculture, Washington (United States)

2012-10-15

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

Study on Development of Non-Destructive Measurement Technique for Viability of Lettuce Seed (Lactuca sativa L) Using Hyperspectral Reflectance Imaging

International Nuclear Information System (INIS)

Ahn, Chi Kook; Cho, Byoung Kwan; Mo, Chang Yeon; Kim, Moon S.

2012-01-01

In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the discrimination of viable and non-viable lettuce seeds. The spectral data of hyperspectral reflectance images with the spectral range between 750 nm and 1000 nm were used to develop PLS-DA model for the classification of viable and non-viable lettuce seeds. The discrimination accuracy of the calibration set was 81.6% and that of the test set was 81.2%. The image analysis method was developed to construct the discriminant images of non-viable seeds with the developed PLS-DA model. The discrimination accuracy obtained from the resultant image were 91%, which showed the feasibility of hyperspectral reflectance imaging technique for the mass discrimination of non-viable lettuce seeds from viable ones.

Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L. Seeds Using LED-Induced Hyperspectral Reflectance Imaging

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Changyeun Mo

2014-04-01

Full Text Available In this study, we developed a viability evaluation method for pepper (Capsicum annuum L. seeds based on hyperspectral reflectance imaging. The reflectance spectra of pepper seeds in the 400–700 nm range are collected from hyperspectral reflectance images obtained using blue, green, and red LED illumination. A partial least squares–discriminant analysis (PLS-DA model is developed to classify viable and non-viable seeds. Four spectral ranges generated with four types of LEDs (blue, green, red, and RGB, which were pretreated using various methods, are investigated to develop the classification models. The optimal PLS-DA model based on the standard normal variate for RGB LED illumination (400–700 nm yields discrimination accuracies of 96.7% and 99.4% for viable seeds and nonviable seeds, respectively. The use of images based on the PLS-DA model with the first-order derivative of a 31.5-nm gap for red LED illumination (600–700 nm yields 100% discrimination accuracy for both viable and nonviable seeds. The results indicate that a hyperspectral imaging technique based on LED light can be potentially applied to high-quality pepper seed sorting.

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

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Zhan-yu LIU

2008-09-01

Full Text Available Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 (the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it's at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Reflectance spectroscopy of pigmented cutaneous benign and malignant lesions

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Borisova, E.; Jeliazkova, Al.; Pavlova, E.; Troyanova, P.; Kundurdjiev, T.; Pavlova, P.; Avramov, L.

2014-10-01

For the DRS measurements of skin benign, dysplastic and malignant lesions in vivo we applied halogen lamp (LS-1, OceanOptics Inc, Dunedin, Fl, USA) as a continuous light source in the region of 400-900 nm, optical probe (6+1 fibers) for the delivery of illumination and diffuse reflected light from the skin investigated and microspectrometer USB4000 (OceanOptics Inc., Dunedin, Fl, USA) for a storage and display of the spectra detected. As a diffuse reflectance standard Spectralon® plate was used to calibrate the spectrometer. The reflectance spectra obtained from normal skin in identical anatomic sites of different patients have similar spectral shape features, slightly differ by the reflectance intensity at different wavelengths, depending on the particular patient' skin phototype. One could find diagnostically important spectral features, related to specific intensity changes for a given wavelength due to specific pigments appearance, slope changes by value and sign for the reflectance spectra curves in a specific spectral range, disappearance or manifestation of minima, related to hemoglobin absorption at 410-420 nm, 543, 575 nm. Based on the observed peculiarities multispectral analysis of the reflectance spectra of the different lesions was used and diagnostically specific features are found. Discrimination using the DRS data obtained between benign compound and dermal nevi (45 cases), dysplastic nevi (17 cases) and pigmented malignant melanoma (41 cases) lesions is achieved with a diagnostic accuracy of 96 % for the benign nevi vs. MM, and 90 % for the dysplastic nevi vs. MM.

Using hyperspectral imaging to determine germination of native Australian plant seeds.

Science.gov (United States)

Nansen, Christian; Zhao, Genpin; Dakin, Nicole; Zhao, Chunhui; Turner, Shane R

2015-04-01

We investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6nm to 878.9nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30days of experimental ageing. P50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism

Assessing Nitrogen Treatment Efficiency in Schima Superba Seedlings Detected Using Hyperspectral Reflectance

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Miaomiao Cheng

2014-01-01

Full Text Available The sharp change in nitrate (N deposition fluxes due to anthropogenic influences has major consequences for terrestrial plant productivity. Early detection of plants under nitrate stress is important for forest management in the subtropical region. This study used leaf-scale hyperspectral reflectance measurements to detect the seedling growth response of Schima superba (S. superba under simulated N deposition during a period of two years. Two-year-old S. superba seedlings were planted under natural field conditions and treated with four N treatments at CK, LN-6, MN-10, and HN-24g N m-2 year-1. The chlorophyll content and leaf reflectance were examined to detect the N addition temporal effects. Results indicated that S. superba responded significantly with differences in chlorophyll content and leaf reflectance to N additional treatment. Compared with the N deficiency (CK plots, plots with higher N addition rate (HN reduced the chlorophyll concentration of S. superba seedlings. However, the long-term observed impact of LN and MN treatments increased the S. superba chlorophyll during the two years. Nitrogen additional treatments can be distinguished using the hyperspectral indices (R700/R720, R695/R420, and R695/R760 retrieved from the differences in leaf reflectance at the green spectrum and the red spectrum. The derivative shift to longer wavelength peaks with increasing N supply, accompanied by the increase in chlorophyll content. Leaf reflectance at 559 nm was negatively correlated with leaf chlorophyll content (R = -0.77. The identified N specific spectral ratios may be used for image interpretation and plant N status diagnosis for site-specific N management.

Plant pigment types, distributions, and influences on shallow water submerged aquatic vegetation mapping

Science.gov (United States)

Hall, Carlton R.; Bostater, Charles R., Jr.; Virnstein, Robert

2004-11-01

Development of robust protocols for use in mapping shallow water habitats using hyperspectral imagery requires knowledge of absorbing and scattering features present in the environment. These include, but are not limited to, water quality parameters, phytoplankton concentrations and species, submerged aquatic vegetation (SAV) species and densities, epiphytic growth on SAV, benthic microalgae and substrate reflectance characteristics. In the Indian River Lagoon, Fl. USA we conceptualize the system as having three possible basic layers, water column and SAV bed above the bottom. Each layer is occupied by plants with their associated light absorbing pigments that occur in varying proportions and concentrations. Phytoplankton communities are composed primarily of diatoms, dinoflagellates, and picoplanktonic cyanobacteria. SAV beds, including flowering plants and green, red, and brown macro-algae exist along density gradients ranging in coverage from 0-100%. SAV beds may be monotypic, or more typically, mixtures of the several species that may or may not be covered in epiphytes. Shallow water benthic substrates are colonized by periphyton communities that include diatoms, dinoflagellates, chlorophytes and cyanobacteria. Inflection spectra created form ASIA hyperspectral data display a combination of features related to water and select plant pigment absorption peaks.

Relative Pigment Composition and Remote Sensing Reflectance of Caribbean Shallow-Water Corals.

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Juan L Torres-Pérez

Full Text Available Reef corals typically contain a number of pigments, mostly due to their symbiotic relationship with photosynthetic dinoflagellates. These pigments usually vary in presence and concentration and influence the spectral characteristics of corals. We studied the variations in pigment composition among seven Caribbean shallow-water Scleractinian corals by means of High Performance Liquid Chromatography (HPLC analysis to further resolve the discrimination of corals. We found a total of 27 different pigments among the coral species, including some alteration products of the main pigments. Additionally, pigments typically found in endolithic algae were also identified. A Principal Components Analysis and a Hierarchical Cluster Analysis showed the separation of coral species based on pigment composition. All the corals were collected under the same physical environmental conditions. This suggests that pigment in the coral's symbionts might be more genetically-determined than influenced by prevailing physical conditions of the reef. We further investigated the use of remote sensing reflectance (Rrs as a tool for estimating the total pigment concentration of reef corals. Depending on the coral species, the Rrs and the total symbiont pigment concentration per coral tissue area correlation showed 79.5-98.5% confidence levels demonstrating its use as a non-invasive robust technique to estimate pigment concentration in studies of coral reef biodiversity and health.

Relative Pigment Composition and Remote Sensing Reflectance of Caribbean Shallow-Water Corals.

Science.gov (United States)

Torres-Pérez, Juan L; Guild, Liane S; Armstrong, Roy A; Corredor, Jorge; Zuluaga-Montero, Anabella; Polanco, Ramón

2015-01-01

Reef corals typically contain a number of pigments, mostly due to their symbiotic relationship with photosynthetic dinoflagellates. These pigments usually vary in presence and concentration and influence the spectral characteristics of corals. We studied the variations in pigment composition among seven Caribbean shallow-water Scleractinian corals by means of High Performance Liquid Chromatography (HPLC) analysis to further resolve the discrimination of corals. We found a total of 27 different pigments among the coral species, including some alteration products of the main pigments. Additionally, pigments typically found in endolithic algae were also identified. A Principal Components Analysis and a Hierarchical Cluster Analysis showed the separation of coral species based on pigment composition. All the corals were collected under the same physical environmental conditions. This suggests that pigment in the coral's symbionts might be more genetically-determined than influenced by prevailing physical conditions of the reef. We further investigated the use of remote sensing reflectance (Rrs) as a tool for estimating the total pigment concentration of reef corals. Depending on the coral species, the Rrs and the total symbiont pigment concentration per coral tissue area correlation showed 79.5-98.5% confidence levels demonstrating its use as a non-invasive robust technique to estimate pigment concentration in studies of coral reef biodiversity and health.

Relationships between pigment composition variation and reflectance for plant species from a coastal savannah in California

Science.gov (United States)

Ustin, Susan L.; Sanderson, Eric W.; Grossman, Yaffa; Hart, Quinn J.

1993-01-01

Advances in imaging spectroscopy have indicated that remotely sensed reflectance measurements of the plant canopy may be used to identify and qualify some classes of canopy biochemicals; however, the manner in which differences in biochemical compositions translate into differences is not well understood. Most frequently, multiple linear regression routines have been used to correlate narrow band reflectance values with measured biochemical concentrations. Although some success has been achieved with such methods for given data sets, the bands selected by multiple regression are not consistent between data sets, nor is it always clear what physical or biological basis underlies the correlation. To examine the relationship between biochemical concentration and leaf reflectance signal we chose to focus on the visible spectrum where the primary biochemical absorbances are due to photosynthetic pigments. Pigments provide a range of absorbance features, occur over a range of concentrations in natural samples, and are ecophysiologically important. Concentrations of chlorophyll, for example, have been strongly correlated to foliar nitrogen levels within a species and to photosynthetic capacity across many species. In addition pigments effectively absorb most of the photosynthetically active radiation between 400-700 nm, a spectral region for which silicon detectors have good signal/noise characteristics. Our strategy has been to sample a variety of naturally occurring species to measure leaf reflectance and pigment compositions. We hope to extend our understanding of pigment reflectance effects to interpret small overlapping absorbances of other biochemicals in the infrared region. For this reason, selected samples were also tested to determine total nitrogen, crude protein, cellulose, and lignin levels. Leaf reflectance spectra measured with AVIRIS bandwidths and wavelengths were compared between species and within species and for differences between seasons, for changes

Reconstructing in-vivo reflectance spectrum of pigmented skin lesion by Monte Carlo simulation

Science.gov (United States)

Wang, Shuang; He, Qingli; Zhao, Jianhua; Lui, Harvey; Zeng, Haishan

2012-03-01

In dermatology applications, diffuse reflectance spectroscopy has been extensively investigated as a promising tool for the noninvasive method to distinguish melanoma from benign pigmented skin lesion (nevus), which is concentrated with the skin chromophores like melanin and hemoglobin. We carried out a theoretical study to examine melanin distribution in human skin tissue and establish a practical optical model for further pigmented skin investigation. The theoretical simulation was using junctional nevus as an example. A multiple layer skin optical model was developed on established anatomy structures of skin, the published optical parameters of different skin layers, blood and melanin. Monte Carlo simulation was used to model the interaction between excitation light and skin tissue and rebuild the diffuse reflectance process from skin tissue. A testified methodology was adopted to determine melanin contents in human skin based on in vivo diffuse reflectance spectra. The rebuild diffuse reflectance spectra were investigated by adding melanin into different layers of the theoretical model. One of in vivo reflectance spectra from Junctional nevi and their surrounding normal skin was studied by compare the ratio between nevus and normal skin tissue in both the experimental and simulated diffuse reflectance spectra. The simulation result showed a good agreement with our clinical measurements, which indicated that our research method, including the spectral ratio method, skin optical model and modifying the melanin content in the model, could be applied in further theoretical simulation of pigmented skin lesions.

Hyperspectral radiometric observation of the northeast Arabians Sea during April 2006

Digital Repository Service at National Institute of Oceanography (India)

Sarangi, R.K.; Singh, S.; Dwivedi, R.M.; Matondkar, S.G.P.

nm. Detailed analysis with High Performance Liquid Chromatography (HPLC) data and comparison with the water composition of hyperspectral radiometer results show that the marine cyanophyte, Trichodesmium bloom produces high pigment concentrations...

Leaf Surface Effects on Retrieving Chlorophyll Content from Hyperspectral Remote Sensing

Science.gov (United States)

Qiu, Feng; Chen, JingMing; Ju, Weimin; Wang, Jun; Zhang, Qian

2017-04-01

Light reflected directly from the leaf surface without entering the surface layer is not influenced by leaf internal biochemical content. Leaf surface reflectance varies from leaf to leaf due to differences in the surface roughness features and is relatively more important in strong absorption spectral regions. Therefore it introduces dispersion of data points in the relationship between biochemical concentration and reflectance (especially in the visible region). Separation of surface from total leaf reflection is important to improve the link between leaf pigments content and remote sensing data. This study aims to estimate leaf surface reflectance from hyperspectral remote sensing data and retrieve chlorophyll content by inverting a modified PROSPECT model. Considering leaf surface reflectance is almost the same in the visible and near infrared spectral regions, a surface layer with a reflectance independent of wavelength but varying from leaf to leaf was added to the PROSPECT model. The specific absorption coefficients of pigments were recalibrated. Then the modified model was inverted on independent datasets to check the performance of the model in predicting the chlorophyll content. Results show that differences in estimated surface layer reflectance of various species are noticeable. Surface reflectance of leaves with epicuticular waxes and trichomes is usually higher than other samples. Reconstruction of leaf reflectance and transmittance in the 400-1000 nm wavelength region using the modified PROSPECT model is excellent with low root mean square error (RMSE) and bias. Improvements for samples with high surface reflectance (e.g. maize) are significant, especially for high pigment leaves. Moreover, chlorophyll retrieved from inversion of the modified model is consequently improved (RMSE from 5.9-13.3 ug/cm2 with mean value 8.1 ug/cm2, while mean correlation coefficient is 0.90) compared to results of PROSPECT-5 (RMSE from 9.6-20.2 ug/cm2 with mean value 13

EFFECTS OF FATLIQURING PROCESS ON LEATHERS COLOURED WITH IR REFLECTIVE DYES AND PIGMENTS

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MUTLU Mehmet Mete

2017-05-01

Full Text Available Black coloured materials and consumer goods are known to be heating up more, because they absorb sun radiation more than light colours. This heating is a problem for the users for black automotive or motorcycle leathers and also for dark shoes and boots which are exposed to sun heat. Human vision system can distinguish visible colours between the wavelengths of 390-700 nm. So reflecting the sun radiation in the infrared area of radiation spectrum higher than 700nm, is a solution for heating problem without affecting the visible colour. For this reason IR reflective dyes and pigments are designed. A leading Leather Chemical Company has developed an IR reflecting dyeing system for leather keeping the dark coloured leathers cooler under sun radiation. Additionally in theory, fat and water content of leather affects its heating properties. In this study, effect of natural, synthetic and waterproof fatliquoring systems on heating properties of leathers coloured with IR reflective dyes and pigments are investigated.

Investigating Bidirectional Reflectance in the Los Angeles Megacity Using CLARS Multiangle and Hyperspectral Measurements

Science.gov (United States)

Zeng, Z. C.; Natraj, V.; Pongetti, T.; Shia, R. L.; Sander, S. P.; Yung, Y. L.

2017-12-01

The surface reflectance is a key ingredient in the remote sensing of surface and atmospheric properties from space. The determination of atmospheric composition, including greenhouse gas (GHG) and aerosol concentrations, from reflected sunlight requires accurate knowledge of the contribution from the underlying surface. Over megacity areas, such as the Los Angeles (LA) basin, which are major sources of GHGs and anthropogenic aerosols, the quantification of surface reflectance is challenging due to the associated complex land use types. In this study, we investigate the bidirectional reflectance in the Los Angeles megacity area using multiangle and hyperspectral radiance measurements from the California Laboratory for Atmospheric Remote Sensing (CLARS). The CLARS facility is located near the top of Mt. Wilson, at an altitude of 1670 m a.s.l., overlooking the LA megacity area with an FTS operating since 2011 to continuously monitor the GHGs and near-surface aerosols in the basin. The CLARS-FTS offers continuous high-resolution spectral measurements in the visible, near infrared and shortwave infrared spectral regions. The CLARS measurements mimic the off-nadir viewing of a low-Earth orbiting instrument, such as GOSAT and OCO-2, but with daily viewing capability. Eight surface targets with different land use types, including urban parks, industrial and residential areas, are selected in this study. The surface reflectance for specific solar incident and viewing angles is calculated by dividing, for non-absorbing spectral channels on clear days (such that gas and aerosol extinction can be ignored), the observed radiance reflected from surface targets by the observed irradiance. The non-linear Rahman-Pinty-Verstraete (RPV) model is used to model the Bidirectional Reflectance Distribution Function (BRDF) by fitting the multiangle and hyperspectral measurements. By evaluating the retrieved RPV parameters, we find that the RPV model provides a good representation of the

Collection and corrections of oblique multiangle hyperspectral bidirectional reflectance imagery of the water surface

Science.gov (United States)

Bostater, Charles R.; Oney, Taylor S.

2017-10-01

Hyperspectral images of coastal waters in urbanized regions were collected from fixed platform locations. Surf zone imagery, images of shallow bays, lagoons and coastal waters are processed to produce bidirectional reflectance factor (BRF) signatures corrected for changing viewing angles. Angular changes as a function of pixel location within a scene are used to estimate changes in pixel size and ground sampling areas. Diffuse calibration targets collected simultaneously from within the image scene provides the necessary information for calculating BRF signatures of the water surface and shorelines. Automated scanning using a pushbroom hyperspectral sensor allows imagery to be collected on the order of one minute or less for different regions of interest. Imagery is then rectified and georeferenced using ground control points within nadir viewing multispectral imagery via image to image registration techniques. This paper demonstrates the above as well as presenting how spectra can be extracted along different directions in the imagery. The extraction of BRF spectra along track lines allows the application of derivative reflectance spectroscopy for estimating chlorophyll-a, dissolved organic matter and suspended matter concentrations at or near the water surface. Imagery is presented demonstrating the techniques to identify subsurface features and targets within the littoral and surf zones.

Hyperspectral Vehicle BRDF Learning: An Exploration of Vehicle Reflectance Variation and Optimal Measures of Spectral Similarity for Vehicle Reacquisition and Tracking Algorithms

Science.gov (United States)

Svejkosky, Joseph

The spectral signatures of vehicles in hyperspectral imagery exhibit temporal variations due to the preponderance of surfaces with material properties that display non-Lambertian bi-directional reflectance distribution functions (BRDFs). These temporal variations are caused by changing illumination conditions, changing sun-target-sensor geometry, changing road surface properties, and changing vehicle orientations. To quantify these variations and determine their relative importance in a sub-pixel vehicle reacquisition and tracking scenario, a hyperspectral vehicle BRDF sampling experiment was conducted in which four vehicles were rotated at different orientations and imaged over a six-hour period. The hyperspectral imagery was calibrated using novel in-scene methods and converted to reflectance imagery. The resulting BRDF sampled time-series imagery showed a strong vehicle level BRDF dependence on vehicle shape in off-nadir imaging scenarios and a strong dependence on vehicle color in simulated nadir imaging scenarios. The imagery also exhibited spectral features characteristic of sampling the BRDF of non-Lambertian targets, which were subsequently verified with simulations. In addition, the imagery demonstrated that the illumination contribution from vehicle adjacent horizontal surfaces significantly altered the shape and magnitude of the vehicle reflectance spectrum. The results of the BRDF sampling experiment illustrate the need for a target vehicle BRDF model and detection scheme that incorporates non-Lambertian BRDFs. A new detection algorithm called Eigenvector Loading Regression (ELR) is proposed that learns a hyperspectral vehicle BRDF from a series of BRDF measurements using regression in a lower dimensional space and then applies the learned BRDF to make test spectrum predictions. In cases of non-Lambertian vehicle BRDF, this detection methodology performs favorably when compared to subspace detections algorithms and graph-based detection algorithms that

Improved heuristics for early melanoma detection using multimode hyperspectral dermoscopy (Conference Presentation)

Science.gov (United States)

Vasefi, Fartash; MacKinnon, Nicholas B.; Booth, Nicholas; Farkas, Daniel L.

2017-02-01

Purpose: To determine the performance of a multimode dermoscopy system (SkinSpect) designed to quantify and 3-D map in vivo melanin and hemoglobin concentrations in skin and its melanoma scoring system, and compare the results accuracy with SIAscopy, and histopathology. Methods: A multimode imaging dermoscope is presented that combines polarization, fluorescence and hyperspectral imaging to accurately map the distribution of skin melanin, collagen and hemoglobin in pigmented lesions. We combine two depth-sensitive techniques: polarization, and hyperspectral imaging, to determine the spatial distribution of melanin and hemoglobin oxygenation in a skin lesion. By quantifying melanin absorption in pigmented areas, we can also more accurately estimate fluorescence emission distribution mainly from skin collagen. Results and discussion: We compared in vivo features of melanocytic lesions (N = 10) extracted by non-invasive SkinSpect and SIMSYS-MoleMate SIAscope, and correlate them to pathology report. Melanin distribution at different depths as well as hemodynamics including abnormal vascularity we detected will be discussed. We will adapt SkinSpect scoring with ABCDE (asymmetry , border, color, diameter, evolution) and seven point dermatologic checklist including: (1) atypical pigment network, (2) blue-whitish veil, (3) atypical vascular pattern, (4) irregular streaks, (5) irregular pigmentation, (6) irregular dots and globules, (7) regression structures estimated by dermatologist. Conclusion: Distinctive, diagnostic features seen by SkinSpect in melanoma vs. normal pigmented lesions will be compared by SIAscopy and results from histopathology.

Unusual development of light-reflecting pigment cells in intact and regenerating tail in the periodic albino mutant of Xenopus laevis.

Science.gov (United States)

Fukuzawa, Toshihiko

2010-10-01

Unusual light-reflecting pigment cells, "white pigment cells", specifically appear in the periodic albino mutant (a(p) /a(p)) of Xenopus laevis and localize in the same place where melanophores normally differentiate in the wild-type. The mechanism responsible for the development of unusual pigment cells is unclear. In this study, white pigment cells in the periodic albino were compared with melanophores in the wild-type, using a cell culture system and a tail-regenerating system. Observations of both intact and cultured cells demonstrate that white pigment cells are unique in (1) showing characteristics of melanophore precursors at various stages of development, (2) accumulating reflecting platelets characteristic of iridophores, and (3) exhibiting pigment dispersion in response to α-melanocyte stimulating hormone (α-MSH) in the same way that melanophores do. When a tadpole tail is amputated, a functionally competent new tail is regenerated. White pigment cells appear in the mutant regenerating tail, whereas melanophores differentiate in the wild-type regenerating tail. White pigment cells in the mutant regenerating tail are essentially similar to melanophores in the wild-type regenerating tail with respect to their localization, number, and response to α-MSH. In addition to white pigment cells, iridophores which are never present in the intact tadpole tail appear specifically in the somites near the amputation level in the mutant regenerating tail. Iridophores are distinct from white pigment cells in size, shape, blue light-induced fluorescence, and response to α-MSH. These findings strongly suggest that white pigment cells in the mutant arise from melanophore precursors and accumulate reflecting platelets characteristic of iridophores.

Simulation of Hyperspectral Images

Science.gov (United States)

Richsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

2004-01-01

A software package generates simulated hyperspectral imagery for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport, as well as reflections from surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, "ground truth" is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces, as well as the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for, and a supplement to, field validation data.

A new application of hyperspectral radiometry: the characterization of painted surfaces

Science.gov (United States)

Wang, Cong; Salvatici, Teresa; Camaiti, Mara; Del Ventisette, Chiara; Moretti, Sandro

2016-04-01

Hyperspectral sensors, working in the Visible-Near Infrared and Short Wave Infrared (VNIR-SWIR) regions, are widely employed for geological applications since they can discriminate many inorganic (e.g. mineral phases) and organic compounds (i.e. vegetations and soils) [1]. Their advantage is to work in the portion of the solar spectrum used for remote sensors. Some examples of application of the hyperspectral sensors to the conservation of cultural heritage are also known. These applications concern the detection of gypsum on historical buildings [2], and the monitoring of organic protective materials on stone surfaces [3]. On the contrary, hyperspectral radiometry has not been employed on painted surfaces. Indeed, the characterization of these surfaces is mainly performed with sophisticated, micro-destractive and time-consuming laboratory analyses (i.e. SEM-EDS, FTIR and, GC-MS spectroscopy) or through portable and non-invasive instruments (mid FTIR, micro Raman, XRF, FORS) which work in different spectral ranges [4,5]. In this work the discrimination of many organic and inorganic components from paintings was investigated through a hyperspectral spectroradiometer ,which works in the 350-2500 nm region. The reflectance spectra were collected by the contact reflectance probe, equipped with an internal light source with fixed geometry of illumination and shot. Several standards samples, selected among the most common materials of paintings, were prepared and analysed in order to collect reference spectra. The standards were prepared with powders of 7 pure pigments, films of 5 varnishes (natural and synthetic), and films of 3 dried binding media. Monochromatic painted surfaces have also been prepared and investigated to verify the identification of different compounds on the surface. The results show that the discrimination of pure products is possible in the VNIR-SWIR region, except for compounds with similar composition (e.g. natural resins such as dammar and

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

Science.gov (United States)

Thenkabail, Prasad S.; Mariotto, Isabella; Gumma, Murali Krishna; Middleton, Elizabeth M.; Landis, David R.; Huemmrich, K. Fred

2013-01-01

.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Portable laser-induced breakdown spectroscopy/diffuse reflectance hybrid spectrometer for analysis of inorganic pigments

Science.gov (United States)

Siozos, Panagiotis; Philippidis, Aggelos; Anglos, Demetrios

2017-11-01

A novel, portable spectrometer, combining two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and diffuse reflectance spectroscopy, was developed with the aim to provide an enhanced instrumental and methodological approach with regard to the analysis of pigments in objects of cultural heritage. Technical details about the hybrid spectrometer and its operation are presented and examples are given relevant to the analysis of paint materials. Both LIBS and diffuse reflectance spectra in the visible and part of the near infrared, corresponding to several neat mineral pigment samples, were recorded and the complementary information was used to effectively distinguish different types of pigments even if they had similar colour or elemental composition. The spectrometer was also employed in the analysis of different paints on the surface of an ancient pottery sherd demonstrating the capabilities of the proposed hybrid diagnostic approach. Despite its instrumental simplicity and compact size, the spectrometer is capable of supporting analytical campaigns relevant to archaeological, historical or art historical investigations, particularly when quick data acquisition is required in the context of surveys of large numbers of objects and samples.

Hyperspectral reflectance of leaves and flowers of an outbreak species discriminates season and successional stage of vegetation

Science.gov (United States)

Carvalho, Sabrina; Schlerf, Martin; van der Putten, Wim H.; Skidmore, Andrew K.

2013-10-01

Spectral reflectance can be used to assess large-scale performances of plants in the field based on plant nutrient balance as well as composition of defence compounds. However, plant chemical composition is known to vary with season - due to its phenology - and it may even depend on the succession stage of its habitat. Here we investigate (i) how spectral reflectance could be used to discriminate successional and phenological stages of Jacobaea vulgaris in both leaf and flower organs and (ii) if chemical content estimation by reflectance is flower or leaf dependent. We used J. vulgaris, which is a natural outbreak plant species on abandoned arable fields in north-western Europe and studied this species in a chronosequence representing successional development during time since abandonment. The chemical content and reflectance between 400 and 2500 nm wavelengths of flowers and leaves were measured throughout the season in fields of different successional ages. The data were analyzed with multivariate statistics for temporal discrimination and estimation of chemical contents in both leaf and flower organs. Two main effects were revealed by spectral reflectance measurements: (i) both flower and leaf spectra show successional and seasonal changes, but the pattern is complex and organ specific (ii) flower head pyrrolizidine alkaloids, which are involved in plant defence against herbivores, can be detected through hyperspectral reflectance.We conclude that spectral reflectance of both leaves and flowers can provide information on plant performance during season and successional stages. As a result, remote sensing studies of plant performance in complex field situations will benefit from considering hyperspectral reflectance of different plant organs. This approach may enable more detailed studies on the link between spectral information and plant defence dynamics both aboveground and belowground.

Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging.

Science.gov (United States)

Li, Jiangbo; Rao, Xiuqin; Ying, Yibin

2012-01-15

Automated discrimination of fruits with canker from other fruit with normal surface and different type of peel defects has become a helpful task to enhance the competitiveness and profitability of the citrus industry. Over the last several years, hyperspectral imaging technology has received increasing attention in the agricultural products inspection field. This paper studied the feasibility of classification of citrus canker from other peel conditions including normal surface and nine peel defects by hyperspectal imaging. A combination algorithm based on principal component analysis and the two-band ratio (Q(687/630)) method was proposed. Since fewer wavelengths were desired in order to develop a rapid multispectral imaging system, the canker classification performance of the two-band ratio (Q(687/630)) method alone was also evaluated. The proposed combination approach and two-band ratio method alone resulted in overall classification accuracy for training set samples and test set samples of 99.5%, 84.5% and 98.2%, 82.9%, respectively. The proposed combination approach was more efficient for classifying canker against various conditions under reflectance hyperspectral imagery. However, the two-band ratio (Q(687/630)) method alone also demonstrated effectiveness in discriminating citrus canker from normal fruit and other peel diseases except for copper burn and anthracnose. Copyright © 2011 Society of Chemical Industry.

Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping

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Victoria Gonzalez-Dugo

2015-10-01

Full Text Available There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI, but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat. The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices (i.e., NDVI did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method, and carotenoids pigment indices (PRI and CAR demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high

Classification of M1/M2-polarized human macrophages by label-free hyperspectral reflectance confocal microscopy and multivariate analysis.

Science.gov (United States)

Bertani, Francesca R; Mozetic, Pamela; Fioramonti, Marco; Iuliani, Michele; Ribelli, Giulia; Pantano, Francesco; Santini, Daniele; Tonini, Giuseppe; Trombetta, Marcella; Businaro, Luca; Selci, Stefano; Rainer, Alberto

2017-08-21

The possibility of detecting and classifying living cells in a label-free and non-invasive manner holds significant theranostic potential. In this work, Hyperspectral Imaging (HSI) has been successfully applied to the analysis of macrophagic polarization, given its central role in several pathological settings, including the regulation of tumour microenvironment. Human monocyte derived macrophages have been investigated using hyperspectral reflectance confocal microscopy, and hyperspectral datasets have been analysed in terms of M1 vs. M2 polarization by Principal Components Analysis (PCA). Following PCA, Linear Discriminant Analysis has been implemented for semi-automatic classification of macrophagic polarization from HSI data. Our results confirm the possibility to perform single-cell-level in vitro classification of M1 vs. M2 macrophages in a non-invasive and label-free manner with a high accuracy (above 98% for cells deriving from the same donor), supporting the idea of applying the technique to the study of complex interacting cellular systems, such in the case of tumour-immunity in vitro models.

Estimating water stressed dwarf green bean pigment concentration through hyperspectral indices

International Nuclear Information System (INIS)

Koksal, E.S.; Ustrun, H.; Ozcan, H.; Gunturk, A.

2010-01-01

In this study, the relationship between leaf pigment concentration (analyzed in the laboratory) and four spectral indexes (measured in the field) was investigated. For this purpose, field experiments consisting of six different irrigation treatments were conducted with dwarf green beans during 2005 growing season. Based on spectral data, spectral indexes were plotted against pigment concentration. Results showed that under water stress, the chlorophyll and carotene contents of green bean leaves rose. According to linear regression analysis between spectral indexes and pigment contents, the Normalized Difference Pigment Chlorophyll Index (NPCI) and Normalized Difference Vegetation Index (NDVI) had the highest correlations with the chlorophyll (a, b and total), and carotene content of leaves. (author)

Synthesis, characterization and optical properties of a high NIR reflecting yellow inorganic pigment: Mo6+ doped Y2Ce2O7 as a cool colorant

International Nuclear Information System (INIS)

Vishnu, V.S.; Reddy, M.L.P.

2010-01-01

Full text: Pigments possessing the ability to confer high solar reflectance have received considerable attention in recent years. The inorganic class of NIR reflective pigments are mainly metal oxides and are primarily employed in two applications: (i) visual camouflage and (ii) reducing heat build up. More than half of the solar radiation consists of near-infrared radiation (52%), the remaining being 43% visible light and 5% ultraviolet radiation. Over heating due to solar radiation negatively affects comfort in the built environment and contributes substantially to electrical consumption for air conditioning and release of green house gases. A pigment which has strong reflections in the NIR region (780-2500 nm) can be referred to as a 'cool' pigment. However, most of the NIR reflective inorganic pigments particularly yellow (eg. cadmium yellow, lead chromate, chrome titanate yellow etc.) contain toxic metals and hence their consumption is being limited. Replacing them with environmentally benign cool pigments that absorb less NIR radiation can yield coatings similar in color, but with higher NIR reflectance. A new class of yellow inorganic pigments possessing high near-infrared reflectance (above 90% at 1100 nm), having the general formula Y 2 Ce 2-x Mo x O 7+δ (x ranges from 0 to 0.5) were synthesized by traditional solid state route. The synthesized samples were characterized by powder X-ray diffraction, Scanning Electron Microscopy, UV-Vis-NIR Diffuse Reflectance Spectroscopy, CIE 1976Lab color scales and TG/DTA analysis. XRD analysis reveals the existence of a major cubic fluorite phase for the pigment samples. The diffuse reflectance analysis of the pigments shows a significant shift in the absorption edge towards higher wavelengths (from 410 nm to 506 nm) for the molybdenum doped samples in comparison with the parent compound. The band gap of the designed pigments changes from 3.01 to 2.44 eV and displays colors varying from ivory white to yellow. The

INFORMATION EXTRACTION IN TOMB PIT USING HYPERSPECTRAL DATA

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X. Yang

2018-04-01

Full Text Available Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Information Extraction in Tomb Pit Using Hyperspectral Data

Science.gov (United States)

Yang, X.; Hou, M.; Lyu, S.; Ma, S.; Gao, Z.; Bai, S.; Gu, M.; Liu, Y.

2018-04-01

Hyperspectral data has characteristics of multiple bands and continuous, large amount of data, redundancy, and non-destructive. These characteristics make it possible to use hyperspectral data to study cultural relics. In this paper, the hyperspectral imaging technology is adopted to recognize the bottom images of an ancient tomb located in Shanxi province. There are many black remains on the bottom surface of the tomb, which are suspected to be some meaningful texts or paintings. Firstly, the hyperspectral data is preprocessing to get the reflectance of the region of interesting. For the convenient of compute and storage, the original reflectance value is multiplied by 10000. Secondly, this article uses three methods to extract the symbols at the bottom of the ancient tomb. Finally we tried to use morphology to connect the symbols and gave fifteen reference images. The results show that the extraction of information based on hyperspectral data can obtain a better visual experience, which is beneficial to the study of ancient tombs by researchers, and provides some references for archaeological research findings.

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

Science.gov (United States)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

Analytical characterization of artists' pigments used in old and modern paintings by total-reflection x-ray fluorescence

International Nuclear Information System (INIS)

Klockenkaemper, R.; Bohlen, A. von; Moens, L.; Devos, W.

1993-01-01

The analytical characterization of artists' pigments is a most helpful tool for art history, conservation and restoration of paintings. A very gentle method of ultra-microsampling was developed that is especially applicable to paintings under restoration. It provides a sample mass of about 1 μg and is virtually non-destructive. This minute amount is sufficient for total-reflection X-ray fluorescence (TXRF) to determine most of those elements building inorganic pigments. The convenient and fast method was applied to oil paintings. Various pigments were identified and their mixing proportion was determined even quantitatively. (author)

UV durable colour pigment doped SmA liquid crystal composites for outdoor trans-reflective bi-stable displays

Science.gov (United States)

Xu, H.; Davey, A. B.; Crossland, W. A.; Chu, D. P.

2012-10-01

High brightness trans-reflective bi-stable displays based on smectic A (SmA) liquid crystals (LCs) can have nearly perfect transparency in the clear state and very high reflection in the scattered state. Because the LC material in use is stable under UV radiation, this kind of displays can stand for strong day-light and therefore be ideal for outdoor applications from e-books to public signage and advertisement. However, the colour application has been limited because the traditional colourants in use are conventional dyes which are lack of UV stability and that their colours are easily photo bleached. Here we present a colour SmA display demonstrator using pigments as colourant. Mixing pigments with SmA LCs and maintain the desirable optical switching performance is not straightforward. We show here how it can be done, including how to obtain fine sized pigment nano-particles, the effects of particle size and size distribution on the display performance. Our optimized pigments/SmA compositions can be driven by a low frequency waveform (~101Hz) to a scattered state to exhibit colour while by a high frequency waveform (~103Hz) to a cleared state showing no colour. Finally, we will present its excellent UV life-time (at least <7.2 years) in comparison with that of dye composition (~2.4 years). The complex interaction of pigment nano-particles with LC molecules and the resulting effects on the LC electro-optical performances are still to be fully understood. We hope this work will not only demonstrate a new and practical approach for outdoor reflective colour displays but also provide a new material system for fundamental liquid crystal colloid research work.

Processing OMEGA/Mars Express hyperspectral imagery from radiance-at-sensor to surface reflectance

NARCIS (Netherlands)

Bakker, W.H.; Ruitenbeek, F.J.A. van; Werff, H.M.A. van der; Zegers, T.E.; Oosthoek, J.H.P.; Marsh, S.H.; Meer, F.D. van der

2014-01-01

OMEGA/Mars Express hyperspectral imagery is an excellent source of data for exploring the surface composition of the planet Mars. Compared to terrestrial hyperspectral imagery, the data are challenging to work with; scene-specific transmission models are lacking, spectral features are shallow making

Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis

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Ye Sun

2018-04-01

Full Text Available Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches were considered for classification by deep belief network (DBN and partial least squares discriminant analysis (PLSDA in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Correction for reflected sky radiance in low-altitude coastal hyperspectral images.

Science.gov (United States)

Kim, Minsu; Park, Joong Yong; Kopilevich, Yuri; Tuell, Grady; Philpot, William

2013-11-10

Low-altitude coastal hyperspectral imagery is sensitive to reflections of sky radiance at the water surface. Even in the absence of sun glint, and for a calm water surface, the wide range of viewing angles may result in pronounced, low-frequency variations of the reflected sky radiance across the scan line depending on the solar position. The variation in reflected sky radiance can be obscured by strong high-spatial-frequency sun glint and at high altitude by path radiance. However, at low altitudes, the low-spatial-frequency sky radiance effect is frequently significant and is not removed effectively by the typical corrections for sun glint. The reflected sky radiance from the water surface observed by a low-altitude sensor can be modeled in the first approximation as the sum of multiple-scattered Rayleigh path radiance and the single-scattered direct-solar-beam radiance by the aerosol in the lower atmosphere. The path radiance from zenith to the half field of view (FOV) of a typical airborne spectroradiometer has relatively minimal variation and its reflected radiance to detector array results in a flat base. Therefore the along-track variation is mostly contributed by the forward single-scattered solar-beam radiance. The scattered solar-beam radiances arrive at the water surface with different incident angles. Thus the reflected radiance received at the detector array corresponds to a certain scattering angle, and its variation is most effectively parameterized using the downward scattering angle (DSA) of the solar beam. Computation of the DSA must account for the roll, pitch, and heading of the platform and the viewing geometry of the sensor along with the solar ephemeris. Once the DSA image is calculated, the near-infrared (NIR) radiance from selected water scan lines are compared, and a relationship between DSA and NIR radiance is derived. We then apply the relationship to the entire DSA image to create an NIR reference image. Using the NIR reference image

[Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

Science.gov (United States)

Tan, Chang-Wei; Zhou, Qing-Bo; Qi, La; Zhuang, Heng-Yang

2008-06-01

The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.

The synthesis, characterization and optical properties of Si4+ and Pr4+ doped Y6 MoO12 compounds: environmentally benign inorganic pigments with high NIR reflectance

International Nuclear Information System (INIS)

George, Giable; Reddy, M.L.P.

2010-01-01

Full text: Much interest has attended roofing materials with high solar reflectance and high thermal emittance, so that interiors stay cool, thereby reducing the demand for air conditioned buildings. The heat producing region of the infrared radiations ranges from 700-1100 nm. Replacing conventional pigments with 'cool pigments' that absorb less NIR radiation can provide coatings similar in color to that of conventional roofing materials, but with higher solar reflectance. NIR reflective pigments have been used in the military, construction, plastics and ink industries. Complex inorganic pigments based on mixed metal oxides (eg., chromium green, cobalt blue, cadmium stannate, lead chromate, cadmium yellow and chrome titanate yellow), which have been used in camouflage, absorb visible light but reflect the NIR portion of incident radiation. However, many of these pigments are toxic and there is a need to develop novel colored, NIR-reflecting inorganic pigments that are less hazardous to the environment. In this work, a series of NIR reflective colored pigments of formula Y 6-x M x MoO 12+δ (where M Si 4+ or Pr 4+ and x ranges from 0 to 1.0) were synthesized by traditional solid-state route and applied to asbestos cement roofing material so as to evaluate their use as 'cool pigments'. The phase purity of the calcined pigment samples were determined using powder X-ray diffraction. The diffuse reflectance of the powdered pigment samples were measured using a UV-Vis-NIR Spectrometer. The Lab color coordinates were evaluated by CIE 1976 color scale. Replacing Si 4+ for Y 3+ in Y 6 MoO 12 changed the color from light-yellow to dark-yellow and the band gap decreased from 2.60 to 2.45 eV due to O 2p -Mo 4d charge transfer transitions. In contrast, replacing Pr 4+ for Y 3+ changed the color from light yellow to dark brown and the band gap shifted from 2.60 to 1.90 eV. The coloring mechanism is based on the introduction of an additional 4f 1 electron energy level of Pr 4

HYPERSPECTRAL AUTOFLUORESCENCE IMAGING OF DRUSEN AND RETINAL PIGMENT EPITHELIUM IN DONOR EYES WITH AGE-RELATED MACULAR DEGENERATION.

Science.gov (United States)

Tong, Yuehong; Ben Ami, Tal; Hong, Sungmin; Heintzmann, Rainer; Gerig, Guido; Ablonczy, Zsolt; Curcio, Christine A; Ach, Thomas; Smith, R Theodore

2016-12-01

To elucidate the molecular pathogenesis of age-related macular degeneration (AMD) and interpretation of fundus autofluorescence imaging, the authors identified spectral autofluorescence characteristics of drusen and retinal pigment epithelium (RPE) in donor eyes with AMD. Macular RPE/Bruch membrane flat mounts were prepared from 5 donor eyes with AMD. In 12 locations (1-3 per eye), hyperspectral autofluorescence images in 10-nm-wavelength steps were acquired at 2 excitation wavelengths (λex 436, 480 nm). A nonnegative tensor factorization algorithm was used to recover 5 abundant emission spectra and their corresponding spatial localizations. At λex 436 nm, the authors consistently localized a novel spectrum (SDr) with a peak emission near 510 nm in drusen and sub-RPE deposits. Abundant emission spectra seen previously (S0 in Bruch membrane and S1, S2, and S3 in RPE lipofuscin/melanolipofuscin, respectively) also appeared in AMD eyes, with the same shapes and peak wavelengths as in normal tissue. Lipofuscin/melanolipofuscin spectra localizations in AMD eyes varied widely in their overlap with drusen, ranging from none to complete. An emission spectrum peaking at ∼510 nm (λex 436 nm) appears to be sensitive and specific for drusen and sub-RPE deposits. One or more abundant spectra from RPE organelles exhibit characteristic relationships with drusen.

Pigment particles analysis with a total reflection X-ray fluorescence spectrometer: study of influence of instrumental parameters

Energy Technology Data Exchange (ETDEWEB)

Coccato, Alessia; Vandenabeele, Peter [Ghent University, Department of Archaeology, Ghent (Belgium); Vekemans, Bart; Vincze, Laszlo; Moens, Luc [Ghent University, Department of Analytical Chemistry, Ghent (Belgium)

2016-12-15

Total reflection X-ray fluorescence (TXRF) analysis is an excellent tool to determine major, minor and trace elements in minuscule amounts of samples, making this technique very suitable for pigment analysis. Collecting minuscule amounts of pigment material from precious works of art by means of a cotton swab is a well-accepted sampling method, but poses specific challenges when TXRF is to be used for the characterization of the unknown material. (orig.)

Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

Science.gov (United States)

We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

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A. Roncat

2017-09-01

Full Text Available While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a make use of full-waveform information and (b operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA, i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types.

Software for Simulation of Hyperspectral Images

Science.gov (United States)

Richtsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.

2002-01-01

A package of software generates simulated hyperspectral images for use in validating algorithms that generate estimates of Earth-surface spectral reflectance from hyperspectral images acquired by airborne and spaceborne instruments. This software is based on a direct simulation Monte Carlo approach for modeling three-dimensional atmospheric radiative transport as well as surfaces characterized by spatially inhomogeneous bidirectional reflectance distribution functions. In this approach, 'ground truth' is accurately known through input specification of surface and atmospheric properties, and it is practical to consider wide variations of these properties. The software can treat both land and ocean surfaces and the effects of finite clouds with surface shadowing. The spectral/spatial data cubes computed by use of this software can serve both as a substitute for and a supplement to field validation data.

The Bone Black Pigment Identification by Noninvasive, In Situ Infrared Reflection Spectroscopy

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Alessia Daveri

2018-01-01

Full Text Available Two real case studies, an oil painting on woven paper and a cycle of mural paintings, have been presented to validate the use of infrared reflection spectroscopy as suitable technique for the identification of bone black pigment. By the use of the sharp weak band at 2013 cm−1, it has been possible to distinguish animal carbon-based blacks by a noninvasive method. Finally, an attempt for an eventual assignment for the widely used sharp band at 2013 cm−1 is discussed.

Exploiting external reflection FTIR spectroscopy for the in-situ identification of pigments and binders in illuminated manuscripts. Brochantite and posnjakite as a case study

Science.gov (United States)

Zaffino, Chiara; Guglielmi, Vittoria; Faraone, Silvio; Vinaccia, Alessandro; Bruni, Silvia

2015-02-01

In the present work, the use of portable instrumentation allowing in-situ reflection FTIR analyses is exploited to identify the coloring matters of northern-Italian illuminations dating to the XVI century. In order to build a database of spectra, reference paint samples were prepared spreading the pigments on parchment with two different binders, i.e. gum arabic and egg white, used in antiquity. Pigments for the database were chosen considering their use in the Middle Ages and in the Renaissance and their response in the mid- and near-IR region. The reflection FTIR spectra obtained resulted to be dominated by the specular reflection component, allowing the use of the Kramers-Kronig transform to convert them to the more conventional absorbance FTIR spectra. Several pigments could thus be identified in ancient illuminations, even if some green details showed a spectral pattern different with respect to the most common commercial green pigments of the database. Therefore, in addition, basic copper sulfates brochantite and posnjakite were synthesized and characterized. In three green details, posnjakite was identified, both as a pure compound and together with malachite.

Vegetation chlorophyll estimates in the Amazon from multi-angle MODIS observations and canopy reflectance model

Science.gov (United States)

Hilker, Thomas; Galvão, Lênio Soares; Aragão, Luiz E. O. C.; de Moura, Yhasmin M.; do Amaral, Cibele H.; Lyapustin, Alexei I.; Wu, Jin; Albert, Loren P.; Ferreira, Marciel José; Anderson, Liana O.; dos Santos, Victor A. H. F.; Prohaska, Neill; Tribuzy, Edgard; Barbosa Ceron, João Vitor; Saleska, Scott R.; Wang, Yujie; de Carvalho Gonçalves, José Francisco; de Oliveira Junior, Raimundo Cosme; Cardoso Rodrigues, João Victor Figueiredo; Garcia, Maquelle Neves

2017-06-01

As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm-2 (Tapajós tower) to 0.470 μg cm-2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future

Hyperspectral optical imaging of two different species of lepidoptera

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Vukusic Pete

2011-01-01

Full Text Available Abstract In this article, we report a hyperspectral optical imaging application for measurement of the reflectance spectra of photonic structures that produce structural colors with high spatial resolution. The measurement of the spectral reflectance function is exemplified in the butterfly wings of two different species of Lepidoptera: the blue iridescence reflected by the nymphalid Morpho didius and the green iridescence of the papilionid Papilio palinurus. Color coordinates from reflectance spectra were calculated taking into account human spectral sensitivity. For each butterfly wing, the observed color is described by a characteristic color map in the chromaticity diagram and spreads over a limited volume in the color space. The results suggest that variability in the reflectance spectra is correlated with different random arrangements in the spatial distribution of the scales that cover the wing membranes. Hyperspectral optical imaging opens new ways for the non-invasive study and classification of different forms of irregularity in structural colors.

An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

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Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

2017-04-01

Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

Illumination compensation in ground based hyperspectral imaging

Science.gov (United States)

Wendel, Alexander; Underwood, James

2017-07-01

Hyperspectral imaging has emerged as an important tool for analysing vegetation data in agricultural applications. Recently, low altitude and ground based hyperspectral imaging solutions have come to the fore, providing very high resolution data for mapping and studying large areas of crops in detail. However, these platforms introduce a unique set of challenges that need to be overcome to ensure consistent, accurate and timely acquisition of data. One particular problem is dealing with changes in environmental illumination while operating with natural light under cloud cover, which can have considerable effects on spectral shape. In the past this has been commonly achieved by imaging known reference targets at the time of data acquisition, direct measurement of irradiance, or atmospheric modelling. While capturing a reference panel continuously or very frequently allows accurate compensation for illumination changes, this is often not practical with ground based platforms, and impossible in aerial applications. This paper examines the use of an autonomous unmanned ground vehicle (UGV) to gather high resolution hyperspectral imaging data of crops under natural illumination. A process of illumination compensation is performed to extract the inherent reflectance properties of the crops, despite variable illumination. This work adapts a previously developed subspace model approach to reflectance and illumination recovery. Though tested on a ground vehicle in this paper, it is applicable to low altitude unmanned aerial hyperspectral imagery also. The method uses occasional observations of reference panel training data from within the same or other datasets, which enables a practical field protocol that minimises in-field manual labour. This paper tests the new approach, comparing it against traditional methods. Several illumination compensation protocols for high volume ground based data collection are presented based on the results. The findings in this paper are

Formulation comprising silicon microparticles, as a pigment that can absorb visible UV radiation and reflect ir radiation

OpenAIRE

Rodríguez, Marie-Isabelle; Fenollosa Esteve, Roberto; Meseguer, Francisco

2011-01-01

[EN] The invention relates to a formulation characterised in that it comprises silicon microparticles having a size between 0.010 um and 50 um in diameter, and to the use thereof as a pigment that can absorb visible UV radiation and reflect IR radiation.

Reconstruction of hyperspectral reflectance for optically complex turbid inland lakes: test of a new scheme and implications for inversion algorithms.

Science.gov (United States)

Sun, Deyong; Hu, Chuanmin; Qiu, Zhongfeng; Wang, Shengqiang

2015-06-01

A new scheme has been proposed by Lee et al. (2014) to reconstruct hyperspectral (400 - 700 nm, 5 nm resolution) remote sensing reflectance (Rrs(λ), sr-1) of representative global waters using measurements at 15 spectral bands. This study tested its applicability to optically complex turbid inland waters in China, where Rrs(λ) are typically much higher than those used in Lee et al. (2014). Strong interdependence of Rrs(λ) between neighboring bands (≤ 10 nm interval) was confirmed, with Pearson correlation coefficient (PCC) mostly above 0.98. The scheme of Lee et al. (2014) for Rrs(λ) re-construction with its original global parameterization worked well with this data set, while new parameterization showed improvement in reducing uncertainties in the reconstructed Rrs(λ). Mean absolute error (MAERrs(λi)) in the reconstructed Rrs(λ) was mostly -1 between 400 and 700nm, and mean relative error (MRERrs(λi)) was rs(λ) spectra. When Rrs(λ) at the MODIS bands were used to reconstruct the hyperspectral Rrs(λ), MAERrs(λi) was -1 and MRERrs(λi) was rs(λ) at the MERIS bands were used, MAERrs(λi) in the reconstructed hyperspectral Rrs(λ) was -1 and MRERrs(λi) was rs(λ) data using spectral bands that may not exist on satellite sensors.

Radiometric Correction of Multitemporal Hyperspectral Uas Image Mosaics of Seedling Stands

Science.gov (United States)

Markelin, L.; Honkavaara, E.; Näsi, R.; Viljanen, N.; Rosnell, T.; Hakala, T.; Vastaranta, M.; Koivisto, T.; Holopainen, M.

2017-10-01

Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

RADIOMETRIC CORRECTION OF MULTITEMPORAL HYPERSPECTRAL UAS IMAGE MOSAICS OF SEEDLING STANDS

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L. Markelin

2017-10-01

Full Text Available Novel miniaturized multi- and hyperspectral imaging sensors on board of unmanned aerial vehicles have recently shown great potential in various environmental monitoring and measuring tasks such as precision agriculture and forest management. These systems can be used to collect dense 3D point clouds and spectral information over small areas such as single forest stands or sample plots. Accurate radiometric processing and atmospheric correction is required when data sets from different dates and sensors, collected in varying illumination conditions, are combined. Performance of novel radiometric block adjustment method, developed at Finnish Geospatial Research Institute, is evaluated with multitemporal hyperspectral data set of seedling stands collected during spring and summer 2016. Illumination conditions during campaigns varied from bright to overcast. We use two different methods to produce homogenous image mosaics and hyperspectral point clouds: image-wise relative correction and image-wise relative correction with BRDF. Radiometric datasets are converted to reflectance using reference panels and changes in reflectance spectra is analysed. Tested methods improved image mosaic homogeneity by 5 % to 25 %. Results show that the evaluated method can produce consistent reflectance mosaics and reflectance spectra shape between different areas and dates.

Contrast based band selection for optimized weathered oil detection in hyperspectral images

Science.gov (United States)

Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier

2012-09-01

Hyperspectral imagery offers unique benefits for detection of land and water features due to the information contained in reflectance signatures such as the bi-directional reflectance distribution function or BRDF. The reflectance signature directly shows the relative absorption and backscattering features of targets. These features can be very useful in shoreline monitoring or surveillance applications, for example to detect weathered oil. In real-time detection applications, processing of hyperspectral data can be an important tool and Optimal band selection is thus important in real time applications in order to select the essential bands using the absorption and backscatter information. In the present paper, band selection is based upon the optimization of target detection using contrast algorithms. The common definition of the contrast (using only one band out of all possible combinations available within a hyperspectral image) is generalized in order to consider all the possible combinations of wavelength dependent contrasts using hyperspectral images. The inflection (defined here as an approximation of the second derivative) is also used in order to enhance the variations in the reflectance spectra as well as in the contrast spectrua in order to assist in optimal band selection. The results of the selection in term of target detection (false alarms and missed detection) are also compared with a previous method to perform feature detection, namely the matched filter. In this paper, imagery is acquired using a pushbroom hyperspectral sensor mounted at the bow of a small vessel. The sensor is mechanically rotated using an optical rotation stage. This opto-mechanical scanning system produces hyperspectral images with pixel sizes on the order of mm to cm scales, depending upon the distance between the sensor and the shoreline being monitored. The motion of the platform during the acquisition induces distortions in the collected HSI imagery. It is therefore

Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging

Science.gov (United States)

Ghassemi, Pejhman; Wang, Jianting; Melchiorri, Anthony J.; Ramella-Roman, Jessica C.; Mathews, Scott A.; Coburn, James C.; Sorg, Brian S.; Chen, Yu; Joshua Pfefer, T.

2015-12-01

The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements-including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth-were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light-tissue interactions and characterizing biophotonic system performance.

The Comparison Between Nmf and Ica in Pigment Mixture Identification of Ancient Chinese Paintings

Science.gov (United States)

Liu, Y.; Lyu, S.; Hou, M.; Yin, Q.

2018-04-01

Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

THE COMPARISON BETWEEN NMF AND ICA IN PIGMENT MIXTURE IDENTIFICATION OF ANCIENT CHINESE PAINTINGS

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Y. Liu

2018-04-01

Full Text Available Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

Platforms for hyperspectral imaging, in-situ optical and acoustical imaging in urbanized regions

Science.gov (United States)

Bostater, Charles R.; Oney, Taylor

2016-10-01

Hyperspectral measurements of the water surface of urban coastal waters are presented. Oblique bidirectional reflectance factor imagery was acquired made in a turbid coastal sub estuary of the Indian River Lagoon, Florida and along coastal surf zone waters of the nearby Atlantic Ocean. Imagery was also collected using a pushbroom hyperspectral imager mounted on a fixed platform with a calibrated circular mechatronic rotation stage. Oblique imagery of the shoreline and subsurface features clearly shows subsurface bottom features and rip current features within the surf zone water column. In-situ hyperspectral optical signatures were acquired from a vessel as a function of depth to determine the attenuation spectrum in Palm Bay. A unique stationary platform methodology to acquire subsurface acoustic images showing the presence of moving bottom boundary nephelometric layers passing through the acoustic fan beam. The acoustic fan beam imagery indicated the presence of oscillatory subsurface waves in the urbanized coastal estuary. Hyperspectral imaging using the fixed platform techniques are being used to collect hyperspectral bidirectional reflectance factor (BRF) measurements from locations at buildings and bridges in order to provide new opportunities to advance our scientific understanding of aquatic environments in urbanized regions.

Use of infrared hyperspectral imaging as an aid for paint identification

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A. Polak

2016-10-01

Full Text Available Art authentication is a complicated process that often requires the extensive study of high value objects. Although a series of non-destructive techniques is already available for art scientists, new techniques, extending current possibilities, are still required. In this paper, the use of a novel mid-infrared tunable imager is proposed as an active hyperspectral imaging system for art work analysis. The system provides access to a range of wavelengths in the electromagnetic spectrum (2500–3750 nm which are otherwise difficult to access using conventional hyperspectral imaging (HSI equipment. The use of such a tool could be beneficial if applied to the paint classification problem and could help analysts map the diversity of pigments within a given painting. The performance of this tool is demonstrated and compared with a conventional, off-the-shelf HSI system operating in the near infrared spectral region (900–1700 nm. Various challenges associated with laser-based imaging are demonstrated and solutions to these challenges as well as the results of applying classification algorithms to datasets captured using both HSI systems are presented. While the conventional HSI system provides data in which more pigments can be accurately classified, the result of applying the proposed laser-based imaging system demonstrates the validity of this technique for application in art authentication tasks.

Refining the Concept of Combining Hyperspectral and Multi-Angle Sensors for Land Surface Applications

Science.gov (United States)

Simic, Anita

Assessment of leaf and canopy chlorophyll content provides information on plant physiological status; it is related to nitrogen content and hence, photosynthesis process, net primary productivity and carbon budget. In this study, a method is developed for the retrieval of total chlorophyll content (Chlorophyll a+b) per unit leaf and per unit ground area based on improved vegetation structural parameters which are derived using multispectral multi-angle remote sensing data. Structural characteristics such as clumping and gaps within a canopy affect its solar radiation absorption and distribution and impact its reflected radiance acquired by a sensor. One of the main challenges for the remote sensing community is to accurately estimate vegetation structural parameters, which inevitably influence the retrieval of leaf chlorophyll content. Multi-angle optical measurements provide a means to characterize the anisotropy of surface reflectance, which has been shown to contain information on vegetation structural characteristics. Hyperspectral optical measurements, on the other hand, provide a fine spectral resolution at the red-edge, a narrow spectral range between the red and near infra-red spectra, which is particularly useful for retrieving chlorophyll content. This study explores a new refined measurement concept of combining multi-angle and hyperspectral remote sensing that employs hyperspectral signals only in the vertical (nadir) direction and multispectral measurements in two additional (off-nadir) directions within two spectral bands, red and near infra-red (NIR). The refinement has been proposed in order to reduce the redundancy of hyperspectral data at more than one angle and to better retrieve the three-dimensional vegetation structural information by choosing the two most useful angles of measurements. To illustrate that hyperspectral data acquired at multiple angles exhibit redundancy, a radiative transfer model was used to generate off-nadir hyperspectral

Modular spectral imaging system for discrimination of pigments in cells and microbial communities.

Science.gov (United States)

Polerecky, Lubos; Bissett, Andrew; Al-Najjar, Mohammad; Faerber, Paul; Osmers, Harald; Suci, Peter A; Stoodley, Paul; de Beer, Dirk

2009-02-01

Here we describe a spectral imaging system for minimally invasive identification, localization, and relative quantification of pigments in cells and microbial communities. The modularity of the system allows pigment detection on spatial scales ranging from the single-cell level to regions whose areas are several tens of square centimeters. For pigment identification in vivo absorption and/or autofluorescence spectra are used as the analytical signals. Along with the hardware, which is easy to transport and simple to assemble and allows rapid measurement, we describe newly developed software that allows highly sensitive and pigment-specific analyses of the hyperspectral data. We also propose and describe a number of applications of the system for microbial ecology, including identification of pigments in living cells and high-spatial-resolution imaging of pigments and the associated phototrophic groups in complex microbial communities, such as photosynthetic endolithic biofilms, microbial mats, and intertidal sediments. This system provides new possibilities for studying the role of spatial organization of microorganisms in the ecological functioning of complex benthic microbial communities or for noninvasively monitoring changes in the spatial organization and/or composition of a microbial community in response to changing environmental factors.

Quantification of Concentration of Microalgae Anabaena Cylindrica, Coal-bed Methane Water Isolates Nannochloropsis Gaditana and PW-95 in Aquatic Solutions through Hyperspectral Reflectance Measurement and Analytical Model Establishment

Science.gov (United States)

Zhou, Z.; Zhou, X.; Apple, M. E.; Spangler, L.

2017-12-01

Three species of microalgae, Anabaena cylindrica (UTEX # 1611), coal-bed methane water isolates Nannochloropsis gaditana and PW-95 were cultured for the measurements of their hyperspectral profiles in different concentrations. The hyperspectral data were measured by an Analytical Spectral Devices (ASD) spectroradiomter with the spectral resolution of 1 nanometer over the wavelength ranges from 350nm to 1050 nm for samples of microalgae of different concentration. Concentration of microalgae was measured using a Hemocytometer under microscope. The objective of this study is to establish the relation between spectral reflectance and micro-algal concentration so that microalgae concentration can be measured remotely by space- or airborne hyperspectral or multispectral sensors. Two types of analytical models, linear reflectance-concentration model and Lamber-Beer reflectance-concentration model, were established for each species. For linear modeling, the wavelength with the maximum correlation coefficient between the reflectance and concentrations of algae was located and then selected for each species of algae. The results of the linear models for each species are shown in Fig.1(a), in which Refl_1, Refl_2, and Refl_3 represent the reflectance of Anabaena, N. Gaditana, and PW-95 respectively. C1, C2, and C3 represent the Concentrations of Anabaena, N. Gaditana, and PW-95 respectively. The Lamber-Beer models were based on the Lambert-Beer Law, which states that the intensity of light propagating in a substance dissolved in a fully transmitting solvent is directly proportional to the concentration of the substance and the path length of the light through the solution. Thus, for the Lamber-Beer modeling, a wavelength with large absorption in red band was selected for each species. The results of Lambert-Beer models for each species are shown in Fig.1(b). Based on the Lamber-Beer models, the absorption coefficient for the three different species will be quantified.

PET and PVC Separation with Hyperspectral Imagery

Science.gov (United States)

Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; La Marca, Floriana

2015-01-01

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry. PMID:25609050

PET and PVC separation with hyperspectral imagery.

Science.gov (United States)

Moroni, Monica; Mei, Alessandro; Leonardi, Alessandra; Lupo, Emanuela; Marca, Floriana La

2015-01-20

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers--polyethylene terephthalate (PET) and polyvinyl chloride (PVC)--in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900-1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

PET and PVC Separation with Hyperspectral Imagery

Directory of Open Access Journals (Sweden)

Monica Moroni

2015-01-01

Full Text Available Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density. Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET and polyvinyl chloride (PVC—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

An Integrated Field and Hyperspectral Remote Sensing Method for the Estimation of Pigments Content of Stipa Purpurea in Shenzha, Tibet

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Bo Kong

2017-01-01

Full Text Available Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that (1 there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728; (2 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3 for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; (4 the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5 the distribution of Chla is opposite to Chlb and Chls content in water area.

An Inverse Modeling Approach to Estimating Phytoplankton Pigment Concentrations from Phytoplankton Absorption Spectra

Science.gov (United States)

Moisan, John R.; Moisan, Tiffany A. H.; Linkswiler, Matthew A.

2011-01-01

Phytoplankton absorption spectra and High-Performance Liquid Chromatography (HPLC) pigment observations from the Eastern U.S. and global observations from NASA's SeaBASS archive are used in a linear inverse calculation to extract pigment-specific absorption spectra. Using these pigment-specific absorption spectra to reconstruct the phytoplankton absorption spectra results in high correlations at all visible wavelengths (r(sup 2) from 0.83 to 0.98), and linear regressions (slopes ranging from 0.8 to 1.1). Higher correlations (r(sup 2) from 0.75 to 1.00) are obtained in the visible portion of the spectra when the total phytoplankton absorption spectra are unpackaged by multiplying the entire spectra by a factor that sets the total absorption at 675 nm to that expected from absorption spectra reconstruction using measured pigment concentrations and laboratory-derived pigment-specific absorption spectra. The derived pigment-specific absorption spectra were further used with the total phytoplankton absorption spectra in a second linear inverse calculation to estimate the various phytoplankton HPLC pigments. A comparison between the estimated and measured pigment concentrations for the 18 pigment fields showed good correlations (r(sup 2) greater than 0.5) for 7 pigments and very good correlations (r(sup 2) greater than 0.7) for chlorophyll a and fucoxanthin. Higher correlations result when the analysis is carried out at more local geographic scales. The ability to estimate phytoplankton pigments using pigment-specific absorption spectra is critical for using hyperspectral inverse models to retrieve phytoplankton pigment concentrations and other Inherent Optical Properties (IOPs) from passive remote sensing observations.

Comparision of Bathymetry and Bottom Characteristics From Hyperspectral Remote Sensing Data and Shipborne Acoustic Measurements

Science.gov (United States)

McIntyre, M. L.; Naar, D. F.; Carder, K. L.; Howd, P. A.; Lewis, J. M.; Donahue, B. T.; Chen, F. R.

2002-12-01

There is growing interest in applying optical remote sensing techniques to shallow-water geological applications such as bathymetry and bottom characterization. Model inversions of hyperspectral remote-sensing reflectance imagery can provide estimates of bottom albedo and depth. This research was conducted in support of the HyCODE (Hyperspectral Coupled Ocean Dynamics Experiment) project in order to test optical sensor performance and the use of a hyperspectral remote-sensing reflectance algorithm for shallow waters in estimating bottom depths and reflectance. The objective of this project was to compare optically derived products of bottom depths and reflectance to shipborne acoustic measurements of bathymetry and backscatter. A set of three high-resolution, multibeam surveys within an 18 km by 1.5 km shore-perpendicular transect 5 km offshore of Sarasota, Florida were collected at water depths ranging from 8 m to 16 m. These products are compared to bottom depths derived from aircraft remote-sensing data collected with the AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) instrument data by means of a semi-analytical remote sensing reflectance model. The pixel size of the multibeam bathymetry and AVIRIS data are 0.25 m and 10 m, respectively. When viewed at full resolution, the multibeam bathymetry data show small-scale sedimentary bedforms (wavelength ~10m, amplitude ~1m) that are not observed in the lower resolution hyperspectral bathymetry. However, model-derived bottom depths agree well with a smoothed version of the multibeam bathymetry. Depths derived from shipborne hyperspectral measurements were accurate within 13%. In areas where diver observations confirmed biological growth and bioturbation, derived bottom depths were less accurate. Acoustic backscatter corresponds well with the aircraft hyperspectral imagery and in situ measurements of bottom reflectance. Acoustic backscatter was used to define the distribution of different bottom types

Secure and Efficient Transmission of Hyperspectral Images for Geosciences Applications

Science.gov (United States)

Carpentieri, Bruno; Pizzolante, Raffaele

2017-12-01

Hyperspectral images are acquired through air-borne or space-borne special cameras (sensors) that collect information coming from the electromagnetic spectrum of the observed terrains. Hyperspectral remote sensing and hyperspectral images are used for a wide range of purposes: originally, they were developed for mining applications and for geology because of the capability of this kind of images to correctly identify various types of underground minerals by analysing the reflected spectrums, but their usage has spread in other application fields, such as ecology, military and surveillance, historical research and even archaeology. The large amount of data obtained by the hyperspectral sensors, the fact that these images are acquired at a high cost by air-borne sensors and that they are generally transmitted to a base, makes it necessary to provide an efficient and secure transmission protocol. In this paper, we propose a novel framework that allows secure and efficient transmission of hyperspectral images, by combining a reversible invisible watermarking scheme, used in conjunction with digital signature techniques, and a state-of-art predictive-based lossless compression algorithm.

Rapid prototyping of biomimetic vascular phantoms for hyperspectral reflectance imaging

Science.gov (United States)

Ghassemi, Pejhman; Wang, Jianting; Melchiorri, Anthony J.; Ramella-Roman, Jessica C.; Mathews, Scott A.; Coburn, James C.; Sorg, Brian S.; Chen, Yu; Joshua Pfefer, T.

2015-01-01

Abstract. The emerging technique of rapid prototyping with three-dimensional (3-D) printers provides a simple yet revolutionary method for fabricating objects with arbitrary geometry. The use of 3-D printing for generating morphologically biomimetic tissue phantoms based on medical images represents a potentially major advance over existing phantom approaches. Toward the goal of image-defined phantoms, we converted a segmented fundus image of the human retina into a matrix format and edited it to achieve a geometry suitable for printing. Phantoms with vessel-simulating channels were then printed using a photoreactive resin providing biologically relevant turbidity, as determined by spectrophotometry. The morphology of printed vessels was validated by x-ray microcomputed tomography. Channels were filled with hemoglobin (Hb) solutions undergoing desaturation, and phantoms were imaged with a near-infrared hyperspectral reflectance imaging system. Additionally, a phantom was printed incorporating two disjoint vascular networks at different depths, each filled with Hb solutions at different saturation levels. Light propagation effects noted during these measurements—including the influence of vessel density and depth on Hb concentration and saturation estimates, and the effect of wavelength on vessel visualization depth—were evaluated. Overall, our findings indicated that 3-D-printed biomimetic phantoms hold significant potential as realistic and practical tools for elucidating light–tissue interactions and characterizing biophotonic system performance. PMID:26662064

Synthesis, Characterization, and NIR Reflectance of Highly Dispersed NiTiO3 and NiTiO3/TiO2 Composite Pigments

Directory of Open Access Journals (Sweden)

Yuping Tong

2016-01-01

Full Text Available The highly dispersed nanostructured NiTiO3 pigments and NiTiO3/TiO2 composite pigments can be synthesized at relative low temperature. The activation energy of crystal growth of NiTiO3 during calcinations via salt-assistant combustion method is 9.35 kJ/mol. The UV-vis spectra results revealed that the absorbance decreased with the increasing of calcinations temperature due to small size effect of nanometer particles. The optical data of NiTiO3 nanocrystals were analyzed at the near-absorption edge. SEM showed that the obtained NiTiO3 nanocrystals and NiTiO3/TiO2 nanocomposite were composed of highly dispersed spherical-like and spherical particles with uniform size distribution, respectively. The chromatic properties and diffuse reflectance of samples were investigated. The obtained NiTiO3/TiO2 composite samples have higher NIR reflectance than NiTiO3 pigments.

An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework

International Nuclear Information System (INIS)

Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong

2016-01-01

This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the

Hyperspectral imaging using a color camera and its application for pathogen detection

Science.gov (United States)

Yoon, Seung-Chul; Shin, Tae-Sung; Heitschmidt, Gerald W.; Lawrence, Kurt C.; Park, Bosoon; Gamble, Gary

2015-02-01

This paper reports the results of a feasibility study for the development of a hyperspectral image recovery (reconstruction) technique using a RGB color camera and regression analysis in order to detect and classify colonies of foodborne pathogens. The target bacterial pathogens were the six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown in Petri dishes of Rainbow agar. The purpose of the feasibility study was to evaluate whether a DSLR camera (Nikon D700) could be used to predict hyperspectral images in the wavelength range from 400 to 1,000 nm and even to predict the types of pathogens using a hyperspectral STEC classification algorithm that was previously developed. Unlike many other studies using color charts with known and noise-free spectra for training reconstruction models, this work used hyperspectral and color images, separately measured by a hyperspectral imaging spectrometer and the DSLR color camera. The color images were calibrated (i.e. normalized) to relative reflectance, subsampled and spatially registered to match with counterpart pixels in hyperspectral images that were also calibrated to relative reflectance. Polynomial multivariate least-squares regression (PMLR) was previously developed with simulated color images. In this study, partial least squares regression (PLSR) was also evaluated as a spectral recovery technique to minimize multicollinearity and overfitting. The two spectral recovery models (PMLR and PLSR) and their parameters were evaluated by cross-validation. The QR decomposition was used to find a numerically more stable solution of the regression equation. The preliminary results showed that PLSR was more effective especially with higher order polynomial regressions than PMLR. The best classification accuracy measured with an independent test set was about 90%. The results suggest the potential of cost-effective color imaging using hyperspectral image

Land Surface Reflectance Retrieval from Hyperspectral Data Collected by an Unmanned Aerial Vehicle over the Baotou Test Site

Science.gov (United States)

Duan, Si-Bo; Li, Zhao-Liang; Tang, Bo-Hui; Wu, Hua; Ma, Lingling; Zhao, Enyu; Li, Chuanrong

2013-01-01

To evaluate the in-flight performance of a new hyperspectral sensor onboard an unmanned aerial vehicle (UAV-HYPER), a comprehensive field campaign was conducted over the Baotou test site in China on 3 September 2011. Several portable reference reflectance targets were deployed across the test site. The radiometric performance of the UAV-HYPER sensor was assessed in terms of signal-to-noise ratio (SNR) and the calibration accuracy. The SNR of the different bands of the UAV-HYPER sensor was estimated to be between approximately 5 and 120 over the homogeneous targets, and the linear response of the apparent reflectance ranged from approximately 0.05 to 0.45. The uniform and non-uniform Lambertian land surface reflectance was retrieved and validated using in situ measurements, with root mean square error (RMSE) of approximately 0.01–0.07 and relative RMSE of approximately 5%–12%. There were small discrepancies between the retrieved uniform and non-uniform Lambertian land surface reflectance over the homogeneous targets and under low aerosol optical depth (AOD) conditions (AOD = 0.18). However, these discrepancies must be taken into account when adjacent pixels had large land surface reflectance contrast and under high AOD conditions (e.g. AOD = 1.0). PMID:23785513

Multi- and hyperspectral scene modeling

Science.gov (United States)

Borel, Christoph C.; Tuttle, Ronald F.

2011-06-01

This paper shows how to use a public domain raytracer POV-Ray (Persistence Of Vision Raytracer) to render multiand hyper-spectral scenes. The scripting environment allows automatic changing of the reflectance and transmittance parameters. The radiosity rendering mode allows accurate simulation of multiple-reflections between surfaces and also allows semi-transparent surfaces such as plant leaves. We show that POV-Ray computes occlusion accurately using a test scene with two blocks under a uniform sky. A complex scene representing a plant canopy is generated using a few lines of script. With appropriate rendering settings, shadows cast by leaves are rendered in many bands. Comparing single and multiple reflection renderings, the effect of multiple reflections is clearly visible and accounts for 25% of the overall apparent canopy reflectance in the near infrared.

Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities

Directory of Open Access Journals (Sweden)

Alan M. Lefcourt

2016-12-01

Full Text Available A system to take high-resolution Visible/Near Infra-Red (VIS/NIR hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm, respectively, for illumination purposes was designed, built, and tested. Components of the system include a semi-autonomous cart, a gated-intensified camera, a spectral adapter, a frequency-triple Nd:YAG (Neodymium-doped Yttrium Aluminium Garnet laser, and optics to convert the Gaussian laser beam into a line-illumination source. The front wheels of the cart are independently powered by stepper motors that support stepping or continuous motion. When stepping, a spreadsheet is used to program parameters of image sets to be acquired at each step. For example, the spreadsheet can be used to set delays before the start of image acquisitions, acquisition times, and laser attenuation. One possible use of this functionality would be to establish acquisition parameters to facilitate the measurement of fluorescence decay-curve characteristics. The laser and camera are mounted on an aluminum plate that allows the optics to be calibrated in a laboratory setting and then moved to the cart. The system was validated by acquiring images of fluorescence responses of spinach leaves and dairy manure.

An optical scanning subsystem for a UAS-enabled hyperspectral radiometer

Data.gov (United States)

National Aeronautics and Space Administration — Hyperspectral radiometers will be integrated with an optical scanning subsystem to measure remote sensing reflectance spectra over the ocean.  The entire scanning...

Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging.

Science.gov (United States)

Lee, Hoonsoo; Kim, Moon S; Song, Yu-Rim; Oh, Chang-Sik; Lim, Hyoun-Sub; Lee, Wang-Hee; Kang, Jum-Soon; Cho, Byoung-Kwan

2017-03-01

There is a need to minimize economic damage by sorting infected seeds from healthy seeds before seeding. However, current methods of detecting infected seeds, such as seedling grow-out, enzyme-linked immunosorbent assays, the polymerase chain reaction (PCR) and the real-time PCR have a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to evaluate the potential of visible/near-infrared (Vis/NIR) hyperspectral imaging system for detecting bacteria-infected watermelon seeds. A hyperspectral Vis/NIR reflectance imaging system (spectral region of 400-1000 nm) was constructed to obtain hyperspectral reflectance images for 336 bacteria-infected watermelon seeds, which were then subjected to partial least square discriminant analysis (PLS-DA) and a least-squares support vector machine (LS-SVM) to classify bacteria-infected watermelon seeds from healthy watermelon seeds. The developed system detected bacteria-infected watermelon seeds with an accuracy > 90% (PLS-DA: 91.7%, LS-SVM: 90.5%), suggesting that the Vis/NIR hyperspectral imaging system is effective for quarantining bacteria-infected watermelon seeds. The results of the present study show that it is possible to use the Vis/NIR hyperspectral imaging system for detecting bacteria-infected watermelon seeds. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Science.gov (United States)

Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

2014-03-01

This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

Hyperspectral band selection based on consistency-measure of neighborhood rough set theory

International Nuclear Information System (INIS)

Liu, Yao; Xie, Hong; Wang, Liguo; Tan, Kezhu; Chen, Yuehua; Xu, Zhen

2016-01-01

Band selection is a well-known approach for reducing dimensionality in hyperspectral imaging. In this paper, a band selection method based on consistency-measure of neighborhood rough set theory (CMNRS) was proposed to select informative bands from hyperspectral images. A decision-making information system was established by the reflection spectrum of soybeans’ hyperspectral data between 400 nm and 1000 nm wavelengths. The neighborhood consistency-measure, which reflects not only the size of the decision positive region, but also the sample distribution in the boundary region, was used as the evaluation function of band significance. The optimal band subset was selected by a forward greedy search algorithm. A post-pruning strategy was employed to overcome the over-fitting problem and find the minimum subset. To assess the effectiveness of the proposed band selection technique, two classification models (extreme learning machine (ELM) and random forests (RF)) were built. The experimental results showed that the proposed algorithm can effectively select key bands and obtain satisfactory classification accuracy. (paper)

Hyperspectral remote sensing

CERN Document Server

Eismann, Michael

2012-01-01

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. This book provides a holistic treatment that captures its multidisciplinary nature, emphasizing the physical principles of hyperspectral remote sensing.

Biodiversity Measurement Using Indices Based on Hyperspectral Reflectance on the Coast of Lagos

Science.gov (United States)

Omodanisi, E. O.; Salami, A. T.

2013-12-01

Hyperspectral measurements provide explicit measurements which can be used in the analysis of biodiversity change. This study was carried out in the coastal area of Lagos State, Nigeria. The objective of this study was to determine if gasoline seepage affects vegetation species distribution and reflectance; with the view to analyzing the vegetation condition. To evaluate the potential of different reflectance spectroscopy of species, the ASD Handheld2 Spectrometer was used. Three identified impacted plots of 30m by 30m were selected randomly and a control plot established in relatively undisturbed vegetated areas away from but perpendicular to the source of seepage. Each identified plot and the control consisted of five transects and measurement were taken at every 2m with about four reflectance measurement per sample point, to average out differences in reflectance as a result of different leaf angles. The radiance output of the spectrometer was converted into reflectance using the reflectance of a white reference over a standardized white spectralon panel. Indices such as Normalized Differential Vegetation Index, RedEdge Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Ratio Vegetation Index and Volgelmann RedEdge Index 1 were calculated to accurately estimate the chlorophyll content in the vegetation within optimal band wavelength. Shannon-Weiner's index, Spearman's rank correlation and Analysis of Variance were used to analyze the data. Cocos nucifera was observed to be the most dominant species with a relative abundance of 47.27% while Ananas comosus recorded the lowest relative abundance of 21.8%. In the control plot, Cocos nucifera had the highest relative abundance of 42.3% and Mangifera indica with the least relative abundance of 16.7%. The relationship between the indices and chlorophyll content of the vegetation were significantly higher at (p>0.01) for all the indices in all the plots; however, RedEdgeNDVI and VOG1 indices had the

Hyperspectral imaging technology for revealing the original handwritings covered by the same inks

Directory of Open Access Journals (Sweden)

Yuanyuan Lian

2017-01-01

Full Text Available This manuscript presents a preliminary investigation on the applicability of hyperspectral imaging technology for nondestructive and rapid analysis to reveal covered original handwritings. The hyperspectral imager Nuance-Macro was used to collect the reflected light signature of inks from the overlapping parts. The software Nuance1p46 was used to analyze the reflected light signature of inks which shows the covered original handwritings. Different types of black/blue ballpoint pen inks and black/blue gel pen inks were chosen for sample preparation. From the hyperspectral images examined, the covered original handwritings of application were revealed in 90.5%, 69.1%, 49.5%, and 78.6% of the cases. Further, the correlation between the revealing effect and spectral characteristics of the reflected light of inks at the overlapping parts was interpreted through theoretical analysis and experimental verification. The results indicated that when the spectral characteristics of the reflected light of inks at the overlapping parts were the same or very similar to that of the ink that was used to cover the original handwriting, the original handwriting could not be shown. On the contrary, when the spectral characteristics of the reflected light of inks at the overlapping parts were different to that of the ink that was used to cover the original handwriting, the original handwriting was revealed.

Phytoplankton Group Identification Using Simulated and In situ Hyperspectral Remote Sensing Reflectance

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Hongyan Xi

2017-08-01

Full Text Available In the present study we investigate the bio-geo-optical boundaries for the possibility to identify dominant phytoplankton groups from hyperspectral ocean color data. A large dataset of simulated remote sensing reflectance spectra, Rrs(λ, was used. The simulation was based on measured inherent optical properties of natural water and measurements of five phytoplankton light absorption spectra representing five major phytoplankton spectral groups. These simulated data, named as C2X data, contain more than 105 different water cases, including cases typical for clearest natural waters as well as for extreme absorbing and extreme scattering waters. For the simulation the used concentrations of chlorophyll a (representing phytoplankton abundance, Chl, are ranging from 0 to 200 mg m−3, concentrations of non-algal particles, NAP, from 0 to 1,500 g m−3, and absorption coefficients of chromophoric dissolved organic matter (CDOM at 440 nm from 0 to 20 m−1. A second, independent, smaller dataset of simulated Rrs(λ used light absorption spectra of 128 cultures from six phytoplankton taxonomic groups to represent natural variability. Spectra of this test dataset are compared with spectra from the C2X data in order to evaluate to which extent the five spectral groups can be correctly identified as dominant under different optical conditions. The results showed that the identification accuracy is highly subject to the water optical conditions, i.e., contribution of and covariance in Chl, NAP, and CDOM. The identification in the simulated data is generally effective, except for waters with very low contribution by phytoplankton and for waters dominated by NAP, whereas contribution by CDOM plays only a minor role. To verify the applicability of the presented approach for natural waters, a test using in situ Rrs(λ dataset collected during a cyanobacterial bloom in Lake Taihu (China is carried out and the approach predicts blue cyanobacteria to be dominant

Influence of composition and roughness on the pigment mapping of paintings using mid-infrared fiberoptics reflectance spectroscopy (mid-IR FORS) and multivariate calibration.

Science.gov (United States)

Sessa, Clarimma; Bagán, Héctor; García, Jose Francisco

2014-10-01

Mid-infrared fiberoptics reflectance spectroscopy (mid-IR FORS) is a very interesting technique for artwork characterization purposes. However, the fact that the spectra obtained are a mixture of surface (specular) and volume (diffuse) reflection is a significant drawback. The physical and chemical features of the artwork surface may produce distortions in the spectra that hinder comparison with reference databases acquired in transmission mode. Several studies attempted to understand the influence of the different variables and propose procedures to improve the interpretation of the spectra. This article is focused on the application of mid-IR FORS and multivariate calibration to the analysis of easel paintings. The objectives are the evaluation of the influence of the surface roughness on the spectra, the influence of the matrix composition for the classification of unknown spectra, and the capability of obtaining pigment composition mappings. A first evaluation of a fast procedure for spectra management and pigment discrimination is discussed. The results demonstrate the capability of multivariate methods, principal component analysis (PCA), and partial least squares discrimination analysis (PLS-DA), to model the distortions of the reflectance spectra and to delimitate and discriminate areas of uniform composition. The roughness of the painting surface is found to be an important factor affecting the shape and relative intensity of the spectra. A mapping of the major pigments of a painting is possible using mid-IR FORS and PLS-DA when the calibration set is a palette that includes the potential pigments present in the artwork mixed with the appropriate binder and that shows the different paint textures.

Inorganic pigment study of the San Pedro Gonzalez Telmo Sibyls using total reflection X-ray fluorescence

International Nuclear Information System (INIS)

Vazquez, Cristina; Custo, Graciela; Barrio, Nestor; Burucua, Jose; Boeykens, Susana; Marte, Fernando

2010-01-01

This article describes the study carried out on a series of oil paintings on canvas from the eighteenth century that were restored at Centro de Produccion e Investigacion en Restauracion y Conservacion Artistica y Bibliografica - Tarea (CEIRCAB-Tarea), Buenos Aires, Argentina: the San Pedro Gonzalez Telmo Sibyls. Experimental study was undertaken to identify inorganic pigments and the technique used in their confection; and, in this way, try to add information about their local origin. Therefore special emphasis was put to infer technologies used in the manufacturing of these paintings. Elemental analysis was performed by total reflection X-ray fluorescence spectrometry (TXRF) and complemented by optical and polarized light microscopy. Microsampling was carefully done over areas of the paintings which were damaged and where a small additional loss will not be noticed. This investigation has shown that a variety of pigments were used, namely earth pigments (red and yellow ochres), white lead, vermilion, etc., and they were used either pure or in mixtures. This characterization helped conservators in their decisions regarding a better understanding of the deterioration processes. In addition, this research about the material composition allowed the art historians and restorers the possibility to obtain information about where, when or by whom The San Pedro Gonzalez Telmo Sibyls may have been painted.

Inorganic pigment study of the San Pedro Gonzalez Telmo Sibyls using total reflection X-ray fluorescence

Energy Technology Data Exchange (ETDEWEB)

Vazquez, Cristina, E-mail: vazquez@cnea.gov.a [Universidad de Buenos Aires, Facultad de Ingenieria, Paseo Colon 850. C1063ACU, Buenos Aires (Argentina); Comision Nacional de Energia Atomica, Gerencia Quimica, Av. Gral Paz 1499, B1650KNA, San Martin (Argentina); Custo, Graciela, E-mail: custo@cnea.gov.a [Comision Nacional de Energia Atomica, Gerencia Quimica, Av. Gral Paz 1499, B1650KNA, San Martin (Argentina); Barrio, Nestor, E-mail: nbarrio@unsam.edu.a [CEIRCAB-TAREA, Universidad Nacional de San Martin (UNSAM), Escuela de Humanidades, Campus Miguelete, 25 de Mayo y Francia, B1650KNA, San Martin (Argentina); Burucua, Jose, E-mail: gburucua@unsam.edu.a [CEIRCAB-TAREA, Universidad Nacional de San Martin (UNSAM), Escuela de Humanidades, Campus Miguelete, 25 de Mayo y Francia, B1650KNA, San Martin (Argentina); Boeykens, Susana [Universidad de Buenos Aires, Facultad de Ingenieria, Paseo Colon 850. C1063ACU, Buenos Aires (Argentina); Marte, Fernando, E-mail: fmarte@unsam.edu.a [CEIRCAB-TAREA, Universidad Nacional de San Martin (UNSAM), Escuela de Humanidades, Campus Miguelete, 25 de Mayo y Francia, B1650KNA, San Martin (Argentina)

2010-09-15

This article describes the study carried out on a series of oil paintings on canvas from the eighteenth century that were restored at Centro de Produccion e Investigacion en Restauracion y Conservacion Artistica y Bibliografica - Tarea (CEIRCAB-Tarea), Buenos Aires, Argentina: the San Pedro Gonzalez Telmo Sibyls. Experimental study was undertaken to identify inorganic pigments and the technique used in their confection; and, in this way, try to add information about their local origin. Therefore special emphasis was put to infer technologies used in the manufacturing of these paintings. Elemental analysis was performed by total reflection X-ray fluorescence spectrometry (TXRF) and complemented by optical and polarized light microscopy. Microsampling was carefully done over areas of the paintings which were damaged and where a small additional loss will not be noticed. This investigation has shown that a variety of pigments were used, namely earth pigments (red and yellow ochres), white lead, vermilion, etc., and they were used either pure or in mixtures. This characterization helped conservators in their decisions regarding a better understanding of the deterioration processes. In addition, this research about the material composition allowed the art historians and restorers the possibility to obtain information about where, when or by whom The San Pedro Gonzalez Telmo Sibyls may have been painted.

[Analysis of related factors of slope plant hyperspectral remote sensing].

Science.gov (United States)

Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

2014-09-01

In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

Bridging research with innovative products: a compact hyperspectral camera for investigating artworks: a feasibility study

Science.gov (United States)

Cucci, Costanza; Casini, Andrea; Stefani, Lorenzo; Picollo, Marcello; Jussila, Jouni

2017-07-01

For more than a decade, a number of studies and research projects have been devoted to customize hyperspectral imaging techniques to the specific needs of conservation and applications in museum context. A growing scientific literature definitely demonstrated the effectiveness of reflectance hyperspectral imaging for non-invasive diagnostics and highquality documentation of 2D artworks. Additional published studies tackle the problems of data-processing, with a focus on the development of algorithms and software platforms optimised for visualisation and exploitation of hyperspectral bigdata sets acquired on paintings. This scenario proves that, also in the field of Cultural Heritage (CH), reflectance hyperspectral imaging has nowadays reached the stage of mature technology, and is ready for the transition from the R&D phase to the large-scale applications. In view of that, a novel concept of hyperspectral camera - featuring compactness, lightness and good usability - has been developed by SPECIM, Spectral Imaging Ltd. (Oulu, Finland), a company in manufacturing products for hyperspectral imaging. The camera is proposed as new tool for novel applications in the field of Cultural Heritage. The novelty of this device relies in its reduced dimensions and weight and in its user-friendly interface, which make this camera much more manageable and affordable than conventional hyperspectral instrumentation. The camera operates in the 400-1000nm spectral range and can be mounted on a tripod. It can operate from short-distance (tens of cm) to long distances (tens of meters) with different spatial resolutions. The first release of the prototype underwent a preliminary in-depth experimentation at the IFAC-CNR laboratories. This paper illustrates the feasibility study carried out on the new SPECIM hyperspectral camera, tested under different conditions on laboratory targets and artworks with the specific aim of defining its potentialities and weaknesses in its use in the

Remote sensing of soil moisture using airborne hyperspectral data

Science.gov (United States)

The Institute for Technology Development (ITD) has developed an airborne hyperspectral sensor system that collects electromagnetic reflectance data of the terrain. The system consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near Infrare...

Line-Scan Hyperspectral Imaging Techniques for Food Safety and Quality Applications

Directory of Open Access Journals (Sweden)

Jianwei Qin

2017-01-01

Full Text Available Hyperspectral imaging technologies in the food and agricultural area have been evolving rapidly over the past 15 years owing to tremendous interest from both academic and industrial fields. Line-scan hyperspectral imaging is a major method that has been intensively researched and developed using different physical principles (e.g., reflectance, transmittance, fluorescence, Raman, and spatially resolved spectroscopy and wavelength regions (e.g., visible (VIS, near infrared (NIR, and short-wavelength infrared (SWIR. Line-scan hyperspectral imaging systems are mainly developed and used for surface inspection of food and agricultural products using area or line light sources. Some of these systems can also be configured to conduct spatially resolved spectroscopy measurements for internal or subsurface food inspection using point light sources. This paper reviews line-scan hyperspectral imaging techniques, with introduction, demonstration, and summarization of existing and emerging techniques for food and agricultural applications. The main topics include related spectroscopy techniques, line-scan measurement methods, hardware components and systems, system calibration methods, and spectral and image analysis techniques. Applications in food safety and quality are also presented to reveal current practices and future trends of line-scan hyperspectral imaging techniques.

Hyperspectral imaging flow cytometer

Science.gov (United States)

Sinclair, Michael B.; Jones, Howland D. T.

2017-10-25

A hyperspectral imaging flow cytometer can acquire high-resolution hyperspectral images of particles, such as biological cells, flowing through a microfluidic system. The hyperspectral imaging flow cytometer can provide detailed spatial maps of multiple emitting species, cell morphology information, and state of health. An optimized system can image about 20 cells per second. The hyperspectral imaging flow cytometer enables many thousands of cells to be characterized in a single session.

Calibration, characterization, and first results with the Ocean PHILLS hyperspectral imager

Science.gov (United States)

Davis, Curtiss O.; Kappus, Mary E.; Bowles, Jeffrey H.; Fisher, John; Antoniades, John A.; Carney, Megan

1999-10-01

The Ocean Portable Hyperspectral Imager for Low-Light spectroscopy (Ocean PHILLS), is a new hyperspectral imager specifically designed for imaging the coastal ocean. It uses a thinned, backside illuminated CCD for high sensitivity, and an all-reflective spectrograph with a convex grating in an Offner configuration to produce a distortion free image. Here we describe the instrument design and present the results of laboratory calibration and characterization and example results from a two week field experiment imaging the coastal waters off Lee Stocking, Island, Bahamas.

EVALUATING THE POTENTIAL OF SATELLITE HYPERSPECTRAL RESURS-P DATA FOR FOREST SPECIES CLASSIFICATION

Directory of Open Access Journals (Sweden)

O. Brovkina

2016-06-01

Full Text Available Satellite-based hyperspectral sensors provide spectroscopic information in relatively narrow contiguous spectral bands over a large area which can be useful in forestry applications. This study evaluates the potential of satellite hyperspectral Resurs-P data for forest species mapping. Firstly, a comparative study between top of canopy reflectance obtained from the Resurs-P, from the airborne hyperspectral scanner CASI and from field measurement (FieldSpec ASD 4 on selected vegetation cover types is conducted. Secondly, Resurs-P data is tested in classification and verification of different forest species compartments. The results demonstrate that satellite hyperspectral Resurs-P sensor can produce useful informational and show good performance for forest species classification comparable both with forestry map and classification from airborne CASI data, but also indicate that developments in pre-processing steps are still required to improve the mapping level.

Hyperspectral image processing methods

Science.gov (United States)

Hyperspectral image processing refers to the use of computer algorithms to extract, store and manipulate both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. A typical hyperspectral image processing work...

Optimization and design of pigments for heat-insulating coatings

Science.gov (United States)

Wang, Guang-Hai; Zhang, Yue

2010-12-01

This paper reports that heat insulating property of infrared reflective coatings is obtained through the use of pigments which diffuse near-infrared thermal radiation. Suitable structure and size distribution of pigments would attain maximum diffuse infrared radiation and reduce the pigment volume concentration required. The optimum structure and size range of pigments for reflective infrared coatings are studied by using Kubelka—Munk theory, Mie model and independent scattering approximation. Taking titania particle as the pigment embedded in an inorganic coating, the computational results show that core-shell particles present excellent scattering ability, more so than solid and hollow spherical particles. The optimum radius range of core-shell particles is around 0.3 ~ 1.6 μm. Furthermore, the influence of shell thickness on optical parameters of the coating is also obvious and the optimal thickness of shell is 100-300 nm.

Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization

Science.gov (United States)

Hakala, Teemu; Scott, Barry; Theocharous, Theo; Näsi, Roope; Suomalainen, Juha; Greenwell, Claire; Fox, Nigel

2018-01-01

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK). PMID:29751560

Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization.

Science.gov (United States)

Hakala, Teemu; Markelin, Lauri; Honkavaara, Eija; Scott, Barry; Theocharous, Theo; Nevalainen, Olli; Näsi, Roope; Suomalainen, Juha; Viljanen, Niko; Greenwell, Claire; Fox, Nigel

2018-05-03

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK).

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

Science.gov (United States)

Juliano da Silva, Carlos; Pasquini, Celio

2015-01-21

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample

Analysis of hyperspectral fluorescence images for poultry skin tumor inspection

Science.gov (United States)

Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.

2004-02-01

We present a hyperspectral fluorescence imaging system with a fuzzy inference scheme for detecting skin tumors on poultry carcasses. Hyperspectral images reveal spatial and spectral information useful for finding pathological lesions or contaminants on agricultural products. Skin tumors are not obvious because the visual signature appears as a shape distortion rather than a discoloration. Fluorescence imaging allows the visualization of poultry skin tumors more easily than reflectance. The hyperspectral image samples obtained for this poultry tumor inspection contain 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 to 711 nm. The large amount of hyperspectral image data is compressed by use of a discrete wavelet transform in the spatial domain. Principal-component analysis provides an effective compressed representation of the spectral signal of each pixel in the spectral domain. A small number of significant features are extracted from two major spectral peaks of relative fluorescence intensity that have been identified as meaningful spectral bands for detecting tumors. A fuzzy inference scheme that uses a small number of fuzzy rules and Gaussian membership functions successfully detects skin tumors on poultry carcasses. Spatial-filtering techniques are used to significantly reduce false positives.

Hyperspectral estimation of corn fraction of photosynthetically active radiation

International Nuclear Information System (INIS)

Yang Fei; Zhang Bai; Song Kaishan

2008-01-01

Fraction of absorbed photosynthetically active radiation (FPAR) is one of the important variables in many productivity and biomass estimation models, this analyzed the effect of FPAR estimation with hyperspectral information, which could provide the scientific support on the improvement of FPAR estimation, remote sensing data validation, and the other ecological models. Based on the field experiment of corn, this paper analyzed the correlations between FPAR and spectral reflectance or the differential coefficient, and discussed the mechanism of FPAR estimation, studied corn FPAR estimation with reflectance, first differential coefficient, NDVI and RVI. The reflectance of visible bands showed much better correlations with FPAR than near-infrared bands. The correlation curve between FPAR and differential coefficient varied more frequently and greatly than the curve of FPAR and reflectance. Reflectance and differential coefficient both had good regressions with FPAR of the typical single band, with the maximum R2 of 0.791 and 0.882. In a word, differential coefficient and vegetation index were much effective than reflectance for corn FPAR estimating, and the stepwised regression of multibands differential coefficient showed the best regression with R2 of 0.944. 375 nm purpled band and 950 nm near-infraed band absorbed by water showed prodigious potential for FPAR estimating precision. On the whole, vegetation index and differential coefficient have good relationships with FPAR, and could be used for FAPR estimation. It would be effective of choosing right bands and excavating the hyperspectral data to improve FPAR estimating precision

Hyperspectral remote sensing

National Research Council Canada - National Science Library

Eismann, Michael Theodore

2012-01-01

..., and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment...

Study on the Spectral Mixing Model for Mineral Pigments Based on Derivative of Ratio Spectroscopy-Take Vermilion and Stone Yellow for Example

Science.gov (United States)

Zhao, H.; Hao, Y.; Liu, X.; Hou, M.; Zhao, X.

2018-04-01

Hyperspectral remote sensing is a completely non-invasive technology for measurement of cultural relics, and has been successfully applied in identification and analysis of pigments of Chinese historical paintings. Although the phenomenon of mixing pigments is very usual in Chinese historical paintings, the quantitative analysis of the mixing pigments in the ancient paintings is still unsolved. In this research, we took two typical mineral pigments, vermilion and stone yellow as example, made precisely mixed samples using these two kinds of pigments, and measured their spectra in the laboratory. For the mixing spectra, both fully constrained least square (FCLS) method and derivative of ratio spectroscopy (DRS) were performed. Experimental results showed that the mixing spectra of vermilion and stone yellow had strong nonlinear mixing characteristics, but at some bands linear unmixing could also achieve satisfactory results. DRS using strong linear bands can reach much higher accuracy than that of FCLS using full bands.

Physiological interpretation of a hyperspectral time series in a citrus orchard

NARCIS (Netherlands)

Stuckens, J.; Dzikiti, S.; Verstraeten, W.W.; Verreynne, J.S.; Swennen, R.; Coppin, P.

2011-01-01

Hyperspectral remote sensing for monitoring horticultural production systems requires the understanding of how plant physiology, canopy structure, management and solar elevation affect the retrieved canopy reflectance during different stages of the phenological cycle. Hence, the objective of this

Optimization and design of pigments for heat-insulating coatings

International Nuclear Information System (INIS)

Wang Guang-Hai; Zhang Yue

2010-01-01

This paper reports that heat insulating property of infrared reflective coatings is obtained through the use of pigments which diffuse near-infrared thermal radiation. Suitable structure and size distribution of pigments would attain maximum diffuse infrared radiation and reduce the pigment volume concentration required. The optimum structure and size range of pigments for reflective infrared coatings are studied by using Kubelka—Munk theory, Mie model and independent scattering approximation. Taking titania particle as the pigment embedded in an inorganic coating, the computational results show that core-shell particles present excellent scattering ability, more so than solid and hollow spherical particles. The optimum radius range of core-shell particles is around 0.3 ∼ 1.6 μm. Furthermore, the influence of shell thickness on optical parameters of the coating is also obvious and the optimal thickness of shell is 100–300 nm. (condensed matter: electronic structure, electrical, magnetic, and optical properties)

Hyperspectral sensing of forests

Science.gov (United States)

Goodenough, David G.; Dyk, Andrew; Chen, Hao; Hobart, Geordie; Niemann, K. Olaf; Richardson, Ash

2007-11-01

Canada contains 10% of the world's forests covering an area of 418 million hectares. The sustainable management of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of new and improved information products to resource managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory, forest health, foliar biochemistry, biomass, and aboveground carbon than are currently available. This paper surveys recent methods and results in hyperspectral sensing of forests and describes space initiatives for hyperspectral sensing.

Biological and remote sensing perspectives of pigmentation in coral reef organisms.

Science.gov (United States)

Hedley, John D; Mumby, Peter J

2002-01-01

Coral reef communities face unprecedented pressures on local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Optical remote sensing, from satellites or aircraft, is possibly the only means of measuring the effects of such stresses at appropriately large spatial scales (many thousands of square kilometres). To map key variables such as coral community structure, percentages of living coral or percentages of dead coral, a remote sensing instrument must be able to distinguish the reflectance spectra (i.e. "spectral signature", reflected light as a function of wavelength) of each category. For biotic classes, reflectance is a complex function of pigmentation, structure and morphology. Studies of coral "colour" fall into two disparate but potentially complementary types. Firstly, biological studies tend to investigate the structure and significance of pigmentation in reef organisms. These studies often lack details that would be useful from a remote sensing perspective such as intraspecific variation in pigment concentration or the contribution of fluorescence to reflectance. Secondly, remote sensing studies take empirical measurements of spectra and seek wavelengths that discriminate benthic categories. Benthic categories used in remote sensing sometimes consist of species groupings that are biologically or spectrally inappropriate (e.g. merging of algal phyla with distinct pigments). Here, we attempt to bridge the gap between biological and remote sensing perspectives of pigmentation in reef taxa. The aim is to assess the extent to which spectral discrimination can be given a biological foundation, to reduce the ad hoc nature of discriminatory criteria, and to understand the fundamental (biological) limitations in the spectral separability of biotic classes. Sources of pigmentation in reef biota are reviewed together with remote sensing studies where spectral discrimination has been effectively demonstrated between benthic

Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

Science.gov (United States)

McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

2016-12-01

Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

Atmospheric correction of APEX hyperspectral data

Directory of Open Access Journals (Sweden)

Sterckx Sindy

2016-03-01

Full Text Available Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC at the Flemish Institute for Technological Research (VITO, Mol, Belgium. The MODerate resolution atmospheric TRANsmission program (MODTRAN is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

[Prediction of Encapsulation Temperatures of Copolymer Films in Photovoltaic Cells Using Hyperspectral Imaging Techniques and Chemometrics].

Science.gov (United States)

Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei

2015-11-01

A novel method of combination of the chemometrics and the hyperspectral imaging techniques was presented to detect the temperatures of Ethylene-Vinyl Acetate copolymer (EVA) films in photovoltaic cells during the thermal encapsulation process. Four varieties of the EVA films which had been heated at the temperatures of 128, 132, 142 and 148 °C during the photovoltaic cells production process were used for investigation in this paper. These copolymer encapsulation films were firstly scanned by the hyperspectral imaging equipment (Spectral Imaging Ltd. Oulu, Finland). The scanning band range of hyperspectral equipemnt was set between 904.58 and 1700.01 nm. The hyperspectral dataset of copolymer films was randomly divided into two parts for the training and test purpose. Each type of the training set and test set contained 90 and 10 instances, respectively. The obtained hyperspectral images of EVA films were dealt with by using the ENVI (Exelis Visual Information Solutions, USA) software. The size of region of interest (ROI) of each obtained hyperspectral image of EVA film was set as 150 x 150 pixels. The average of reflectance hyper spectra of all the pixels in the ROI was used as the characteristic curve to represent the instance. There kinds of chemometrics methods including partial least squares regression (PLSR), multi-class support vector machine (SVM) and large margin nearest neighbor (LMNN) were used to correlate the characteristic hyper spectra with the encapsulation temperatures of of copolymer films. The plot of weighted regression coefficients illustrated that both bands of short- and long-wave near infrared hyperspectral data contributed to enhancing the prediction accuracy of the forecast model. Because the attained reflectance hyperspectral data of EVA materials displayed the strong nonlinearity, the prediction performance of linear modeling method of PLSR declined and the prediction precision only reached to 95%. The kernel-based forecast models were

Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ

Directory of Open Access Journals (Sweden)

Yasser Khouj

2018-02-01

Full Text Available Hyperspectral imaging (HSI is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ.

Science.gov (United States)

Khouj, Yasser; Dawson, Jeremy; Coad, James; Vona-Davis, Linda

2018-01-01

Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

Hyperspectral monitoring of chemically sensitive plant sentinels

Science.gov (United States)

Simmons, Danielle A.; Kerekes, John P.; Raqueno, Nina G.

2009-08-01

Automated detection of chemical threats is essential for an early warning of a potential attack. Harnessing plants as bio-sensors allows for distributed sensing without a power supply. Monitoring the bio-sensors requires a specifically tailored hyperspectral system. Tobacco plants have been genetically engineered to de-green when a material of interest (e.g. zinc, TNT) is introduced to their immediate vicinity. The reflectance spectra of the bio-sensors must be accurately characterized during the de-greening process for them to play a role in an effective warning system. Hyperspectral data have been collected under laboratory conditions to determine the key regions in the reflectance spectra associated with the degreening phenomenon. Bio-sensor plants and control (nongenetically engineered) plants were exposed to TNT over the course of two days and their spectra were measured every six hours. Rochester Institute of Technologys Digital Imaging and Remote Sensing Image Generation Model (DIRSIG) was used to simulate detection of de-greened plants in the field. The simulated scene contains a brick school building, sidewalks, trees and the bio-sensors placed at the entrances to the buildings. Trade studies of the bio-sensor monitoring system were also conducted using DIRSIG simulations. System performance was studied as a function of field of view, pixel size, illumination conditions, radiometric noise, spectral waveband dependence and spectral resolution. Preliminary results show that the most significant change in reflectance during the degreening period occurs in the near infrared region.

Estimating physiological skin parameters from hyperspectral signatures

Science.gov (United States)

Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe

2013-05-01

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.

Pictorial materials database: 1200 combinations of pigments, dyes, binders and varnishes designed as a tool for heritage science and conservation

Science.gov (United States)

Cavaleri, Tiziana; Buscaglia, Paola; Migliorini, Simonetta; Nervo, Marco; Piccablotto, Gabriele; Piccirillo, Anna; Pisani, Marco; Puglisi, Davide; Vaudan, Dario; Zucco, Massimo

2017-06-01

The conservation of artworks requires a profound knowledge about pictorial materials, their chemical and physical properties and their interaction and/or degradation processes. For this reason, pictorial materials databases are widely used to study and investigate cultural heritage. At Centre for Conservation and Restoration La Venaria Reale, we prepared a set of about 1200 mock-ups with 173 different pigments and/or dyes, used across all the historical times or as products for conservation, four binders, two varnishes and four different materials for underdrawings. In collaboration with the Laboratorio Analisi Scientifiche of Regione Autonoma Valle d'Aosta, the National Institute of Metrological Research and the Department of Architecture and Design of the Polytechnic of Turin, we created a scientific database that is now available online (http://www.centrorestaurovenaria.it/en/areas/diagnostic/pictorial-materials-database) designed as a tool for heritage science and conservation. Here, we present a focus on materials for pictorial retouching where the hyperspectral imaging application, conducted with a prototype of new technology, allowed to provide a list of pigments that could be more suitable for conservation treatments and pictorial retouching. Then we present the case study of the industrial painting Notte Barbara (1962) by Pinot Gallizio where the use of the database including modern and contemporary art materials showed to be very useful and where the fibre optics reflectance spectroscopy technique was decisive for pigment identification purpose. Later in this research, the mock-ups will be exploited to study degradation processes, e.g., the lightfastness, or the possible formation of interaction products, e.g., metal carboxylates.

Point-and-stare operation and high-speed image acquisition in real-time hyperspectral imaging

Science.gov (United States)

Driver, Richard D.; Bannon, David P.; Ciccone, Domenic; Hill, Sam L.

2010-04-01

The design and optical performance of a small-footprint, low-power, turnkey, Point-And-Stare hyperspectral analyzer, capable of fully automated field deployment in remote and harsh environments, is described. The unit is packaged for outdoor operation in an IP56 protected air-conditioned enclosure and includes a mechanically ruggedized fully reflective, aberration-corrected hyperspectral VNIR (400-1000 nm) spectrometer with a board-level detector optimized for point and stare operation, an on-board computer capable of full system data-acquisition and control, and a fully functioning internal hyperspectral calibration system for in-situ system spectral calibration and verification. Performance data on the unit under extremes of real-time survey operation and high spatial and high spectral resolution will be discussed. Hyperspectral acquisition including full parameter tracking is achieved by the addition of a fiber-optic based downwelling spectral channel for solar illumination tracking during hyperspectral acquisition and the use of other sensors for spatial and directional tracking to pinpoint view location. The system is mounted on a Pan-And-Tilt device, automatically controlled from the analyzer's on-board computer, making the HyperspecTM particularly adaptable for base security, border protection and remote deployments. A hyperspectral macro library has been developed to control hyperspectral image acquisition, system calibration and scene location control. The software allows the system to be operated in a fully automatic mode or under direct operator control through a GigE interface.

Evaluation of hyperspectral reflectance for estimating dry matter and sugar concentration in processing potatoes

Science.gov (United States)

The measurement of sugar concentration and dry matter in processing potatoes is a time and resource intensive activity, cannot be performed in the field, and does not easily measure within tuber variation. A proposed method to improve the phenotyping of processing potatoes is to employ hyperspectral...

Hyperspectral signatures and WorldView-3 imagery of Indian River Lagoon and Banana River Estuarine water and bottom types

Science.gov (United States)

Bostater, Charles R.; Oney, Taylor S.; Rotkiske, Tyler; Aziz, Samin; Morrisette, Charles; Callahan, Kelby; Mcallister, Devin

2017-10-01

Hyperspectral signatures and imagery collected during the spring and summer of 2017 and 2016 are presented. Ground sampling distances (GSD) and pixel sizes were sampled from just over a meter to less than 4.0 mm. A pushbroom hyperspectral imager was used to calculate bidirectional reflectance factor (BRF) signatures. Hyperspectral signatures of different water types and bottom habitats such as submerged seagrasses, drift algae and algal bloom waters were scanned using a high spectral and digital resolution solid state spectrograph. WorldView-3 satellite imagery with minimal water wave sun glint effects was used to demonstrate the ability to detect bottom features using a derivative reflectance spectroscopy approach with the 1.3 m GSD multispectral satellite channels centered at the solar induced fluorescence band. The hyperspectral remote sensing data collected from the Banana River and Indian River Lagoon watersheds represents previously unknown signatures to be used in satellite and airborne remote sensing of water in turbid waters along the US Atlantic Ocean coastal region and the Florida littoral zone.

Hyperspectral image classifier based on beach spectral feature

International Nuclear Information System (INIS)

Liang, Zhang; Lianru, Gao; Bing, Zhang

2014-01-01

The seashore, especially coral bank, is sensitive to human activities and environmental changes. A multispectral image, with coarse spectral resolution, is inadaptable for identify subtle spectral distinctions between various beaches. To the contrary, hyperspectral image with narrow and consecutive channels increases our capability to retrieve minor spectral features which is suit for identification and classification of surface materials on the shore. Herein, this paper used airborne hyperspectral data, in addition to ground spectral data to study the beaches in Qingdao. The image data first went through image pretreatment to deal with the disturbance of noise, radiation inconsistence and distortion. In succession, the reflection spectrum, the derivative spectrum and the spectral absorption features of the beach surface were inspected in search of diagnostic features. Hence, spectra indices specific for the unique environment of seashore were developed. According to expert decisions based on image spectrums, the beaches are ultimately classified into sand beach, rock beach, vegetation beach, mud beach, bare land and water. In situ surveying reflection spectrum from GER1500 field spectrometer validated the classification production. In conclusion, the classification approach under expert decision based on feature spectrum is proved to be feasible for beaches

Hyperspectral image analysis. A tutorial

International Nuclear Information System (INIS)

Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa

2015-01-01

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares – Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case. - Highlights: • Comprehensive tutorial of Hyperspectral Image analysis. • Hierarchical discrimination of six classes of plastics containing flame retardant. • Step by step guidelines to perform class-modeling on hyperspectral images. • Fusion of multivariate data analysis and digital image processing methods. • Promising methodology for real-time detection of plastics containing flame retardant.

Detection of mechanical injury on pickling cucumbers using near-infrared hyperspectral imaging

Science.gov (United States)

Ariana, D.; Lu, R.; Guyer, D.

2005-11-01

Automated detection of defects on freshly harvested pickling cucumbers will help the pickle industry provide higher quality pickle products and reduce potential economic losses. Research was conducted on using a hyperspectral imaging system for detecting defects on pickling cucumbers caused by mechanical stress. A near-infrared hyperspectral imaging system was used to capture both spatial and spectral information from cucumbers in the spectral region of 900 - 1700 nm. The system consisted of an imaging spectrograph attached to an InGaAs camera with line-light fiber bundles as an illumination source. Cucumber samples were subjected to two forms of mechanical loading, dropping and rolling, to simulate stress caused by mechanical harvesting. Hyperspectral images were acquired from the cucumbers over time periods of 0, 1, 2, 3, and 6 days after mechanical stress. Hyperspectral image processing methods, including principal component analysis and wavelength selection, were developed to separate normal and mechanically injured cucumbers. Results showed that reflectance from normal or non-bruised cucumbers was consistently higher than that from bruised cucumbers. The spectral region between 950 and 1350 nm was found to be most effective for bruise detection. The hyperspectral imaging system detected all mechanically injured cucumbers immediately after they were bruised. The overall detection accuracy was 97% within two hours of bruising and it was lower as time progressed. Lower detection accuracies for the prolonged times after bruising were attributed to the self- healing of the bruised tissue after mechanical injury. This research demonstrated that hyperspectral imaging is useful for detecting mechanical injury on pickling cucumbers.

Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data

Science.gov (United States)

Thenkabail, P.S.; Mariotto, I.; Gumma, M.K.; Middleton, E.M.; Landis, D.R.; Huemmrich, K.F.

2013-01-01

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al

Enabling Searches on Wavelengths in a Hyperspectral Indices Database

Science.gov (United States)

Piñuela, F.; Cerra, D.; Müller, R.

2017-10-01

Spectral indices derived from hyperspectral reflectance measurements are powerful tools to estimate physical parameters in a non-destructive and precise way for several fields of applications, among others vegetation health analysis, coastal and deep water constituents, geology, and atmosphere composition. In the last years, several micro-hyperspectral sensors have appeared, with both full-frame and push-broom acquisition technologies, while in the near future several hyperspectral spaceborne missions are planned to be launched. This is fostering the use of hyperspectral data in basic and applied research causing a large number of spectral indices to be defined and used in various applications. Ad hoc search engines are therefore needed to retrieve the most appropriate indices for a given application. In traditional systems, query input parameters are limited to alphanumeric strings, while characteristics such as spectral range/ bandwidth are not used in any existing search engine. Such information would be relevant, as it enables an inverse type of search: given the spectral capabilities of a given sensor or a specific spectral band, find all indices which can be derived from it. This paper describes a tool which enables a search as described above, by using the central wavelength or spectral range used by a given index as a search parameter. This offers the ability to manage numeric wavelength ranges in order to select indices which work at best in a given set of wavelengths or wavelength ranges.

Hyperspectral imaging and multivariate analysis in the dried blood spots investigations

Science.gov (United States)

Majda, Alicja; Wietecha-Posłuszny, Renata; Mendys, Agata; Wójtowicz, Anna; Łydżba-Kopczyńska, Barbara

2018-04-01

The aim of this study was to apply a new methodology using the combination of the hyperspectral imaging and the dry blood spot (DBS) collecting. Application of the hyperspectral imaging is fast and non-destructive. DBS method offers the advantage also on the micro-invasive blood collecting and low volume of required sample. During experimental step, the reflected light was recorded by two hyperspectral systems. The collection of 776 spectral bands in the VIS-NIR range (400-1000 nm) and 256 spectral bands in the SWIR range (970-2500 nm) was applied. Pixel has the size of 8 × 8 and 30 × 30 µm for VIS-NIR and SWIR camera, respectively. The obtained data in the form of hyperspectral cubes were treated with chemometric methods, i.e., minimum noise fraction and principal component analysis. It has been shown that the application of these methods on this type of data, by analyzing the scatter plots, allows a rapid analysis of the homogeneity of DBS, and the selection of representative areas for further analysis. It also gives the possibility of tracking the dynamics of changes occurring in biological traces applied on the surface. For the analyzed 28 blood samples, described method allowed to distinguish those blood stains because of time of apply.

On the Atmospheric Correction of Antarctic Airborne Hyperspectral Data

Directory of Open Access Journals (Sweden)

Martin Black

2014-05-01

Full Text Available The first airborne hyperspectral campaign in the Antarctic Peninsula region was carried out by the British Antarctic Survey and partners in February 2011. This paper presents an insight into the applicability of currently available radiative transfer modelling and atmospheric correction techniques for processing airborne hyperspectral data in this unique coastal Antarctic environment. Results from the Atmospheric and Topographic Correction version 4 (ATCOR-4 package reveal absolute reflectance values somewhat in line with laboratory measured spectra, with Root Mean Square Error (RMSE values of 5% in the visible near infrared (0.4–1 µm and 8% in the shortwave infrared (1–2.5 µm. Residual noise remains present due to the absorption by atmospheric gases and aerosols, but certain parts of the spectrum match laboratory measured features very well. This study demonstrates that commercially available packages for carrying out atmospheric correction are capable of correcting airborne hyperspectral data in the challenging environment present in Antarctica. However, it is anticipated that future results from atmospheric correction could be improved by measuring in situ atmospheric data to generate atmospheric profiles and aerosol models, or with the use of multiple ground targets for calibration and validation.

Estimating cadmium concentration in the edible part of Capsicum annuum using hyperspectral models.

Science.gov (United States)

Wang, Ting; Wei, Hong; Zhou, Cui; Gu, Yanwen; Li, Rui; Chen, Hongchun; Ma, Wenchao

2017-10-09

Hyperspectral remote sensing can be applied to the rapid and nondestructive monitoring of heavy-metal pollution in crops. To realize the rapid and real-time detection of cadmium in the edible part (fruit) of Capsicum annuum, the leaf spectral reflectance of plants exposed to different levels of cadmium stress was measured using hyperspectral remote sensing during four growth stages. The spectral indices or bands sensitive to cadmium stress were determined by correlation analysis, and hyperspectral estimation models for predicting the cadmium content in the fruit of C. annuum during the mature growth stage were established. The models were cross validated by taking the sensitive spectral indices in the bud stage and the sensitive spectral bands in the flowering stage as the input variables. The results indicated that cadmium accumulated in the leaves and fruit of C. annuum and leaf cadmium content in the three early growth stages were correlated with the cadmium content of the pepper in the mature stage. Leaf spectral reflectance was sensitive to cadmium stress, and the first derivative of the original spectral reflectance was strongly correlated with leaf cadmium content during all growth stages. Among the established models, the multiple regression model based on the sensitive spectral bands in the flowering stage was optimal for predicting fruit cadmium content of the pepper. This model provides a promising method to ensure food safety during the early growth stage of the plant.

On-line fresh-cut lettuce quality measurement system using hyperspectral imaging

Science.gov (United States)

Lettuce, which is a main type of fresh-cut vegetable, has been used in various fresh-cut products. In this study, an online quality measurement system for detecting foreign substances on the fresh-cut lettuce was developed using hyperspectral reflectance imaging. The online detection system with a s...

ANALYSIS OF THE RADIOMETRIC RESPONSE OF ORANGE TREE CROWN IN HYPERSPECTRAL UAV IMAGES

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N. N. Imai

2017-10-01

Full Text Available High spatial resolution remote sensing images acquired by drones are highly relevant data source in many applications. However, strong variations of radiometric values are difficult to correct in hyperspectral images. Honkavaara et al. (2013 presented a radiometric block adjustment method in which hyperspectral images taken from remotely piloted aerial systems – RPAS were processed both geometrically and radiometrically to produce a georeferenced mosaic in which the standard Reflectance Factor for the nadir is represented. The plants crowns in permanent cultivation show complex variations since the density of shadows and the irradiance of the surface vary due to the geometry of illumination and the geometry of the arrangement of branches and leaves. An evaluation of the radiometric quality of the mosaic of an orange plantation produced using images captured by a hyperspectral imager based on a tunable Fabry-Pérot interferometer and applying the radiometric block adjustment method, was performed. A high-resolution UAV based hyperspectral survey was carried out in an orange-producing farm located in Santa Cruz do Rio Pardo, state of São Paulo, Brazil. A set of 25 narrow spectral bands with 2.5 cm of GSD images were acquired. Trend analysis was applied to the values of a sample of transects extracted from plants appearing in the mosaic. The results of these trend analysis on the pixels distributed along transects on orange tree crown showed the reflectance factor presented a slightly trend, but the coefficients of the polynomials are very small, so the quality of mosaic is good enough for many applications.

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

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Ana-Isabel de Castro

2012-01-01

Full Text Available In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC analysis and two neural networks, specifically, multilayer perceptron (MLP and radial basis function (RBF. Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and yield

Science.gov (United States)

Modern hyperspectral sensors permit reflectance measurements of crop canopies in hundreds of narrow spectral wavebands. While these sensors describe plant canopy reflectance in greater detail than multispectral sensors, they also suffer from issues with data redundancy and spectral autocorrelation. ...

Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

Science.gov (United States)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the

A BIOSENSOR USING COUPLED PLASMON WAVEGUIDE RESONANCE COMBINED WITH HYPERSPECTRAL FLUORESCENCE ANALYSIS

Directory of Open Access Journals (Sweden)

CHAN DU

2014-01-01

Full Text Available We developed a biosensor that is capable for simultaneous surface plasmon resonance (SPR sensing and hyperspectral fluorescence analysis in this paper. A symmetrical metal-dielectric slab scheme is employed for the excitation of coupled plasmon waveguide resonance (CPWR in the present work. Resonance between surface plasmon mode and the guided waveguide mode generates narrower full width half-maximum of the reflective curves which leads to increased precision for the determination of refractive index over conventional SPR sensors. In addition, CPWR also offers longer surface propagation depths and higher surface electric field strengths that enable the excitation of fluorescence with hyperspectral technique to maintain an appreciable signal-to-noise ratio. The refractive index information obtained from SPR sensing and the chemical properties obtained through hyperspectral fluorescence analysis confirm each other to exclude false-positive or false-negative cases. The sensor provides a comprehensive understanding of the biological events on the sensor chips.

Subcellular pigment distribution is altered under far-red light acclimation in cyanobacteria that contain chlorophyll f.

Science.gov (United States)

Majumder, Erica L-W; Wolf, Benjamin M; Liu, Haijun; Berg, R Howard; Timlin, Jerilyn A; Chen, Min; Blankenship, Robert E

2017-11-01

Far-Red Light (FRL) acclimation is a process that has been observed in cyanobacteria and algae that can grow solely on light above 700 nm. The acclimation to FRL results in rearrangement and synthesis of new pigments and pigment-protein complexes. In this study, cyanobacteria containing chlorophyll f, Synechococcus sp. PCC 7335 and Halomicronema hongdechloris, were imaged as live cells with confocal microscopy. H. hongdechloris was further studied with hyperspectral confocal fluorescence microscopy (HCFM) and freeze-substituted thin-section transmission electron microscopy (TEM). Under FRL, phycocyanin-containing complexes and chlorophyll-containing complexes were determined to be physically separated and the synthesis of red-form phycobilisome and Chl f was increased. The timing of these responses was observed. The heterogeneity and eco-physiological response of the cells was noted. Additionally, a gliding motility for H. hongdechloris is reported.

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

Directory of Open Access Journals (Sweden)

Christian Nansen

2012-01-01

Full Text Available A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values were examined as possible indicators of abiotic (irrigation regime and biotic (spider mite infestation stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L. at multiple time points in 2 data sets (229 hyperspectral images, and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters, it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods.

Assessment of plant species diversity based on hyperspectral indices at a fine scale.

Science.gov (United States)

Peng, Yu; Fan, Min; Song, Jingyi; Cui, Tiantian; Li, Rui

2018-03-19

Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R 2  = 0.83), Pielou (R 2  = 0.87) and Shannon-Wiener index (R 2  = 0.88). Stepwise linear regression of FD (R 2  = 0.81, R 2  = 0.82) and spectral vegetation indices (R 2  = 0.51, R 2  = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.

Hyperspectral remote sensing techniques for early detection of plant diseases

Science.gov (United States)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas

Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications in Earth observation. Nowadays spectral remote sensing techniques allow presymptomatic monitoring of changes in the physiological state of plants with high spectral resolution. Hyperspectral leaf reflectance and chlorophyll fluorescence proved to be highly suitable for identification of growth anomalies of cultural plants that result from the environmental changes and different stress factors. Hyperspectral technologies can find place in many scientific areas, as well as for monitoring of plants status and functioning to help in making timely management decisions. This research aimed to detect a presence of viral infection in young pepper plants (Capsicum annuum L.) caused by Cucumber Mosaic Virus (CMV) by using hyperspectral reflectance and fluorescence data and to assess the effect of some growth regulators on the development of the disease. In Bulgaria CMV is one of the widest spread pathogens, causing the biggest economical losses in crop vegetable production. Leaf spectral reflectance and fluorescence data were collected by a portable fibre-optics spectrometer in the spectral ranges 450÷850 nm and 600-900 nm. Greenhouse experiment with pepper plants of two cultivars, Sivria (sensitive to CMV) and Ostrion (resistant to CMV) were used. The plants were divided into six groups. The first group consisted of healthy (control) plants. At growth stage 4-6 expanded leaf, the second group was inoculated with CMV. The other four groups were treated with growth regulators: Spermine, MEIA (beta-monomethyl ester of itaconic acid), BTH (benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester) and Phytoxin. On the next day, the pepper plants of these four groups were inoculated with CMV. The viral concentrations in the plants were determined by the serological method DAS-ELISA. Statistical, first derivative and cluster analysis were applied and several vegetation indices were

D Reconstruction from Uav-Based Hyperspectral Images

Science.gov (United States)

Liu, L.; Xu, L.; Peng, J.

2018-04-01

Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications.

Spectral tuning of Amazon parrot feather coloration by psittacofulvin pigments and spongy structures.

Science.gov (United States)

Tinbergen, Jan; Wilts, Bodo D; Stavenga, Doekele G

2013-12-01

The feathers of Amazon parrots are brightly coloured. They contain a unique class of pigments, the psittacofulvins, deposited in both barbs and barbules, causing yellow or red coloured feathers. In specific feather areas, spongy nanostructured barb cells exist, reflecting either in the blue or blue-green wavelength range. The blue-green spongy structures are partly enveloped by a blue-absorbing, yellow-colouring pigment acting as a spectral filter, thus yielding a green coloured barb. Applying reflection and transmission spectroscopy, we characterized the Amazons' pigments and spongy structures, and investigated how they contribute to the feather coloration. The reflectance spectra of Amazon feathers are presumably tuned to the sensitivity spectra of the visual photoreceptors.

Ground Field-Based Hyperspectral Imaging: A Preliminary Study to Assess the Potential of Established Vegetation Indices to Infer Variation in Water-Use Efficiency.

Science.gov (United States)

Pelech, E. A.; McGrath, J.; Pederson, T.; Bernacchi, C.

2017-12-01

Increases in the global average temperature will consequently induce a higher occurrence of severe environmental conditions such as drought on arable land. To mitigate these threats, crops for fuel and food must be bred for higher water-use efficiencies (WUE). Defining genomic variation through high-throughput phenotypic analysis in field conditions has the potential to relieve the major bottleneck in linking desirable genetic traits to the associated phenotypic response. This can subsequently enable breeders to create new agricultural germplasm that supports the need for higher water-use efficient crops. From satellites to field-based aerial and ground sensors, the reflectance properties of vegetation measured by hyperspectral imaging is becoming a rapid high-throughput phenotyping technique. A variety of physiological traits can be inferred by regression analysis with leaf reflectance which is controlled by the properties and abundance of water, carbon, nitrogen and pigments. Although, given that the current established vegetation indices are designed to accentuate these properties from spectral reflectance, it becomes a challenge to infer relative measurements of WUE at a crop canopy scale without ground-truth data collection. This study aims to correlate established biomass and canopy-water-content indices with ground-truth data. Five bioenergy sorghum genotypes (Sorghum bicolor L. Moench) that have differences in WUE and wild-type Tobacco (Nicotiana tabacum var. Samsun) under irrigated and rainfed field conditions were examined. A linear regression analysis was conducted to determine if variation in canopy water content and biomass, driven by natural genotypic and artificial treatment influences, can be inferred using established vegetation indices. The results from this study will elucidate the ability of ground field-based hyperspectral imaging to assess variation in water content, biomass and water-use efficiency. This can lead to improved opportunities to

Hyperspectral image analysis. A tutorial

DEFF Research Database (Denmark)

Amigo Rubio, Jose Manuel; Babamoradi, Hamid; Elcoroaristizabal Martin, Saioa

2015-01-01

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processi...... to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case....... will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology...

A molecular investigation of adsorption onto mineral pigments

Science.gov (United States)

Ninness, Brian J.

Pigment suspensions are important in several processes such as ceramics, paints, inks, and coatings. In the wet state, pigments are combined with a variety of chemical species such as polymers, surfactants, and polyelectrolytes which produce a complex colloidal system. The adsorption, desorption, and redistribution of these species at the pigment-aqueous solution interface can have an impact on the behavior in both the wet state or its final dried state. The goal of this work is to establish a molecular picture of the adsorption properties of these pigmented systems. A novel in situ infrared technique has been developed which allows the detection of adsorbed surface species on pigment particles in an aqueous environment. The technique involves the use of a polymeric binder to anchor the colloidal pigment particles to the surface of an internal reflection element (IRE). The binder only weakly perturbs about 25% of the reactive surface sites (hydroxyl groups) on silica. The reaction of succinic anhydride with an aminosilanized silica surface has been quantified using this technique. The adsorption dynamics of the cationic surfactant cetyltrimethylammonium bromide (C16TAB) at the TiO2-aqueous solution interface has been investigated using Fourier transform infrared-attenuated total reflection spectroscopy (FTIR-ATR) and electrokinetic analysis. At low bulk concentrations, C16TAB is shown to adsorb as isolated islands with a "defective" bilayer structure. Anionic probe molecules are shown to effectively "tune" the adsorbed surfactant microstructure. The results indicate that the structure of the adsorbed surfactant layer, and not the amount of adsorbed surfactant, dictates the subsequent adsorption behavior of the system. Atomic Layer Deposition is used to deposit a TiO2 layer onto the surfaces of silica and kaolin pigments. The process involves the cyclic reaction sequence of the vapors of TiCl4 and H2O. Three complete deposition cycles are needed before the surfaces

Hyperspectral Imaging and SPA-LDA Quantitative Analysis for Detection of Colon Cancer Tissue

Science.gov (United States)

Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.

2018-05-01

Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis-NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis-NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.

Hyperspectral fundus imager

Science.gov (United States)

Truitt, Paul W.; Soliz, Peter; Meigs, Andrew D.; Otten, Leonard John, III

2000-11-01

A Fourier Transform hyperspectral imager was integrated onto a standard clinical fundus camera, a Zeiss FF3, for the purposes of spectrally characterizing normal anatomical and pathological features in the human ocular fundus. To develop this instrument an existing FDA approved retinal camera was selected to avoid the difficulties of obtaining new FDA approval. Because of this, several unusual design constraints were imposed on the optical configuration. Techniques to calibrate the sensor and to define where the hyperspectral pushbroom stripe was located on the retina were developed, including the manufacturing of an artificial eye with calibration features suitable for a spectral imager. In this implementation the Fourier transform hyperspectral imager can collect over a hundred 86 cm-1 spectrally resolved bands with 12 micro meter/pixel spatial resolution within the 1050 nm to 450 nm band. This equates to 2 nm to 8 nm spectral resolution depending on the wavelength. For retinal observations the band of interest tends to lie between 475 nm and 790 nm. The instrument has been in use over the last year successfully collecting hyperspectral images of the optic disc, retinal vessels, choroidal vessels, retinal backgrounds, and macula diabetic macular edema, and lesions of age-related macular degeneration.

APEX - the Hyperspectral ESA Airborne Prism Experiment

Directory of Open Access Journals (Sweden)

Koen Meuleman

2008-10-01

Full Text Available The airborne ESA-APEX (Airborne Prism Experiment hyperspectral mission simulator is described with its distinct specifications to provide high quality remote sensing data. The concept of an automatic calibration, performed in the Calibration Home Base (CHB by using the Control Test Master (CTM, the In-Flight Calibration facility (IFC, quality flagging (QF and specific processing in a dedicated Processing and Archiving Facility (PAF, and vicarious calibration experiments are presented. A preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user is presented for limnology, vegetation, aerosols, general classification routines and rapid mapping tasks. BRDF (Bidirectional Reflectance Distribution Function issues are discussed and the spectral database SPECCHIO (Spectral Input/Output introduced. The optical performance as well as the dedicated software utilities make APEX a state-of-the-art hyperspectral sensor, capable of (a satisfying the needs of several research communities and (b helping the understanding of the Earth’s complex mechanisms.

Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries

Directory of Open Access Journals (Sweden)

Yan-Ru Zhao

2015-01-01

Full Text Available Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR region (380–1030 nm were introduced to assess total soluble solids (TSS in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands and their corresponding TSS contents were measured by a Brix meter. Random frog (RF method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR and least-square support vector machine (LS-SVM models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.

A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on leafy greens

Science.gov (United States)

Ensuring the supply of safe, contaminant free fresh fruit and vegetables is of importance to consumers, suppliers and governments worldwide. In this study, three hyperspectral imaging (HSI) configurations coupled with two multivariate image analysis techniques are compared for detection of fecal con...

Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer

Science.gov (United States)

Goto, Atsushi; Nishikawa, Jun; Kiyotoki, Shu; Nakamura, Munetaka; Nishimura, Junichi; Okamoto, Takeshi; Ogihara, Hiroyuki; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2015-01-01

Hyperspectral imaging (HSI) is a new technology that obtains spectroscopic information and renders it in image form. This study examined the difference in the spectral reflectance (SR) of gastric tumors and normal mucosa recorded with a hyperspectral camera equipped with HSI technology and attempted to determine the specific wavelength that is useful for the diagnosis of gastric cancer. A total of 104 gastric tumors removed by endoscopic submucosal dissection from 96 patients at Yamaguchi University Hospital were recorded using a hyperspectral camera. We determined the optimal wavelength and the cut-off value for differentiating tumors from normal mucosa to establish a diagnostic algorithm. We also attempted to highlight tumors by image processing using the hyperspectral camera's analysis software. A wavelength of 770 nm and a cut-off value of 1/4 the corrected SR were selected as the respective optimal wavelength and cut-off values. The rates of sensitivity, specificity, and accuracy of the algorithm's diagnostic capability were 71%, 98%, and 85%, respectively. It was possible to enhance tumors by image processing at the 770-nm wavelength. HSI can be used to measure the SR in gastric tumors and to differentiate between tumorous and normal mucosa.

Skin Pigmentation Kinetics after Exposure to Ultraviolet A

DEFF Research Database (Denmark)

Ravnbak, M.H.; Philipsen, P.A.; Wiegell, S.R.

2009-01-01

Multiple exposures to ultraviolet radiation (UVR) are the norm in nature and phototherapy. However, studies of the kinetics of pigmentation following UVA exposure have included only fair-skinned persons. The aim of this study was to investigate steady-state pigmentation and fading in 12 Scandinav......Multiple exposures to ultraviolet radiation (UVR) are the norm in nature and phototherapy. However, studies of the kinetics of pigmentation following UVA exposure have included only fair-skinned persons. The aim of this study was to investigate steady-state pigmentation and fading in 12...... Scandinavians and 12 Indians/Pakistanis after 6 and 12 exposures on the back using broadband UVA and UVA1 with equal sub-minimal melanogenic doses (individually predetermined). Pigmentation was measured by skin reflectance at 555 and 660 urn. The UV dose to minimal pigmentation was higher in dark......-skinned persons after a single broadband UVA exposure, but independent of pigmentation/skin type after single and multiple UVA1 exposures. To elicit minimal melanogenic doses after 6 and 12 exposures, every dose is lowered by a factor of 2 and 3, respectively, but the cumulative dose increases three- and four...

Algorithm for retrieving vegetative canopy and leaf parameters from multi- and hyperspectral imagery

Science.gov (United States)

Borel, Christoph

2009-05-01

In recent years hyper-spectral data has been used to retrieve information about vegetative canopies such as leaf area index and canopy water content. For the environmental scientist these two parameters are valuable, but there is potentially more information to be gained as high spatial resolution data becomes available. We developed an Amoeba (Nelder-Mead or Simplex) based program to invert a vegetative canopy radiosity model coupled with a leaf (PROSPECT5) reflectance model and modeled for the background reflectance (e.g. soil, water, leaf litter) to a measured reflectance spectrum. The PROSPECT5 leaf model has five parameters: leaf structure parameter Nstru, chlorophyll a+b concentration Cab, carotenoids content Car, equivalent water thickness Cw and dry matter content Cm. The canopy model has two parameters: total leaf area index (LAI) and number of layers. The background reflectance model is either a single reflectance spectrum from a spectral library() derived from a bare area pixel on an image or a linear mixture of soil spectra. We summarize the radiosity model of a layered canopy and give references to the leaf/needle models. The method is then tested on simulated and measured data. We investigate the uniqueness, limitations and accuracy of the retrieved parameters on canopy parameters (low, medium and high leaf area index) spectral resolution (32 to 211 band hyperspectral), sensor noise and initial conditions.

Snapshot hyperspectral imaging and practical applications

International Nuclear Information System (INIS)

Wong, G

2009-01-01

Traditional broadband imaging involves the digital representation of a remote scene within a reduced colour space. Hyperspectral imaging exploits the full spectral dimension, which better reflects the continuous nature of actual spectra. Conventional techniques are all time-delayed whereby spatial or spectral scanning is required for hypercube generation. An innovative and patented technique developed at Heriot-Watt University offers significant potential as a snapshot sensor, to enable benefits for the wider public beyond aerospace imaging. This student-authored paper seeks to promote awareness of this field within the photonic community and its potential advantages for real-time practical applications.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-11-23

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-01-01

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

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H. Aasen

2016-06-01

Full Text Available Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

Nondestructive detection of total viable count changes of chilled pork in high oxygen storage condition based on hyperspectral technology

Science.gov (United States)

Zheng, Xiaochun; Peng, Yankun; Li, Yongyu; Chao, Kuanglin; Qin, Jianwei

2017-05-01

The plate count method is commonly used to detect the total viable count (TVC) of bacteria in pork, which is timeconsuming and destructive. It has also been used to study the changes of the TVC in pork under different storage conditions. In recent years, many scholars have explored the non-destructive methods on detecting TVC by using visible near infrared (VIS/NIR) technology and hyperspectral technology. The TVC in chilled pork was monitored under high oxygen condition in this study by using hyperspectral technology in order to evaluate the changes of total bacterial count during storage, and then evaluate advantages and disadvantages of the storage condition. The VIS/NIR hyperspectral images of samples stored in high oxygen condition was acquired by a hyperspectral system in range of 400 1100nm. The actual reference value of total bacteria was measured by standard plate count method, and the results were obtained in 48 hours. The reflection spectra of the samples are extracted and used for the establishment of prediction model for TVC. The spectral preprocessing methods of standard normal variate transformation (SNV), multiple scatter correction (MSC) and derivation was conducted to the original reflectance spectra of samples. Partial least squares regression (PLSR) of TVC was performed and optimized to be the prediction model. The results show that the near infrared hyperspectral technology based on 400-1100nm combined with PLSR model can describe the growth pattern of the total bacteria count of the chilled pork under the condition of high oxygen very vividly and rapidly. The results obtained in this study demonstrate that the nondestructive method of TVC based on NIR hyperspectral has great potential in monitoring of edible safety in processing and storage of meat.

Development of Research Infrastructure in Nevada for the Exploitation of Hyperspectral Image Data to Address Proliferation and Detection of Chemical and Biological Materials

International Nuclear Information System (INIS)

James V. Taranik

2007-01-01

This research was to exploit hyperspectral reflectance imaging technology for the detection and mapping variability (clutter) of the natural background against which gases in the atmosphere are imaged. The natural background consists of landscape surface cover composed of consolidated rocks, unconsolidated rock weathering products, soils, coatings on rock materials, vegetation, water, materials constructed by humans, and mixtures of the above. Human made gases in the atmosphere may indicate industrial processes important to detecting non-nuclear chemical and biological proliferation. Our research was to exploit the Visible and Near-Infrared (NIR) and the Short-wave Infrared (SWIR) portions of the electromagnetic spectrum to determine the properties of solid materials on the earth's surface that could influence the detection of gases in the Long-Wave Infrared (LWIR). We used some new experimental hyperspectral imaging technologies to collect data over the Non-Proliferation Test and Evaluation Center (NPTEC) located on the Nevada Test Site (NTS). The SpecTIR HyperSpecTIR (HST) and Specim Dual hyperspectral sensors were used to understand the variability in the imaged background (clutter), that detected, measured, identified and mapped with operational commercial hyperspectral techniques. The HST sensors were determined to be more experimental than operational because of problems with radiometric and atmospheric data correction. However the SpecTIR Dual system, developed by Specim in Finland, eventually was found to provide cost-effective hyperspectral image data collection and it was possible to correct the Dual system's data for specific areas. Batch processing of long flightlines was still complex, and if comparison to laboratory spectra was desired, the Dual system data still had to be processed using the empirical line method. This research determined that 5-meter spatial resolution was adequate for mapping natural background variations. Furthermore, this

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

Science.gov (United States)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water

Hyperspectral image compressing using wavelet-based method

Science.gov (United States)

Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng

2017-10-01

Hyperspectral imaging sensors can acquire images in hundreds of continuous narrow spectral bands. Therefore each object presented in the image can be identified from their spectral response. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space borne imaging. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data. Moreover, most hyperspectral data compression techniques exploits the similarities in spectral dimensions; which requires bands reordering or regrouping, to make use of the spectral redundancy. In this paper, we explored the spectral cross correlation between different bands, and proposed an adaptive band selection method to obtain the spectral bands which contain most of the information of the acquired hyperspectral data cube. The proposed method mainly consist three steps: First, the algorithm decomposes the original hyperspectral imagery into a series of subspaces based on the hyper correlation matrix of the hyperspectral images between different bands. And then the Wavelet-based algorithm is applied to the each subspaces. At last the PCA method is applied to the wavelet coefficients to produce the chosen number of components. The performance of the proposed method was tested by using ISODATA classification method.

Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China

Directory of Open Access Journals (Sweden)

Zhiguo Dou

2018-04-01

Full Text Available The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value. We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves.

Design and Test of Portable Hyperspectral Imaging Spectrometer

Directory of Open Access Journals (Sweden)

Chunbo Zou

2017-01-01

Full Text Available We design and implement a portable hyperspectral imaging spectrometer, which has high spectral resolution, high spatial resolution, small volume, and low weight. The flight test has been conducted, and the hyperspectral images are acquired successfully. To achieve high performance, small volume, and regular appearance, an improved Dyson structure is designed and used in the hyperspectral imaging spectrometer. The hyperspectral imaging spectrometer is suitable for the small platform such as CubeSat and UAV (unmanned aerial vehicle, and it is also convenient to use for hyperspectral imaging acquiring in the laboratory and the field.

Identification of early cancerous lesion of esophagus with endoscopic images by hyperspectral image technique (Conference Presentation)

Science.gov (United States)

Huang, Shih-Wei; Chen, Shih-Hua; Chen, Weichung; Wu, I.-Chen; Wu, Ming Tsang; Kuo, Chie-Tong; Wang, Hsiang-Chen

2016-03-01

This study presents a method to identify early esophageal cancer within endoscope using hyperspectral imaging technology. The research samples are three kinds of endoscopic images including white light endoscopic, chromoendoscopic, and narrow-band endoscopic images with different stages of pathological changes (normal, dysplasia, dysplasia - esophageal cancer, and esophageal cancer). Research is divided into two parts: first, we analysis the reflectance spectra of endoscopic images with different stages to know the spectral responses by pathological changes. Second, we identified early cancerous lesion of esophagus by principal component analysis (PCA) of the reflectance spectra of endoscopic images. The results of this study show that the identification of early cancerous lesion is possible achieve from three kinds of images. In which the spectral characteristics of NBI endoscopy images of a gray area than those without the existence of the problem the first two, and the trend is very clear. Therefore, if simply to reflect differences in the degree of spectral identification, chromoendoscopic images are suitable samples. The best identification of early esophageal cancer is using the NBI endoscopic images. Based on the results, the use of hyperspectral imaging technology in the early endoscopic esophageal cancer lesion image recognition helps clinicians quickly diagnose. We hope for the future to have a relatively large amount of endoscopic image by establishing a hyperspectral imaging database system developed in this study, so the clinician can take this repository more efficiently preliminary diagnosis.

The Final Development Related Microbial Pigments and the Application in Food Industry

OpenAIRE

YANGILAR, Filiz; YILDIZ, Pınar OGUZHAN

2016-01-01

The successful marketing of natural pigments derived from microorganisms and microalgae or extracted from flowering plants, both as food colorants and nutritional supplements, reflects the presence, and importance of markets in which consumers are willing to pay a premium for “natural healthy ingredients”. As known, pigments prefer in natural products have antioxidant, antimicrobial and antimutagenic activities. The most commonly used food grade pigments are chemical compounds containing nitr...

Hyperspectral forest monitoring and imaging implications

Science.gov (United States)

Goodenough, David G.; Bannon, David

2014-05-01

The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

Hyperspectral image processing

CERN Document Server

Wang, Liguo

2016-01-01

Based on the authors’ research, this book introduces the main processing techniques in hyperspectral imaging. In this context, SVM-based classification, distance comparison-based endmember extraction, SVM-based spectral unmixing, spatial attraction model-based sub-pixel mapping, and MAP/POCS-based super-resolution reconstruction are discussed in depth. Readers will gain a comprehensive understanding of these cutting-edge hyperspectral imaging techniques. Researchers and graduate students in fields such as remote sensing, surveying and mapping, geosciences and information systems will benefit from this valuable resource.

The gecko visual pigment: the anion hypsochromic effect.

Science.gov (United States)

Crescitelli, F; Karvaly, B

1991-01-01

The 521-pigment in the retina of the Tokay gecko (Gekko gekko) readily responds to particular physical and chemical changes in its environment. When solubilized in chloride deficient state the addition of Class I anions (Cl-, Br-) induces a bathochromic shift of the absorption spectrum. Class II anions (NO3-, IO3-, N3-, OCN-, SCN-, SeCN-, N(CN)2-), which exhibit ambidental properties, cause an hypsochromic shift. Class III anions (F-, I-, NO2-, CN-, AsO3-, SO2(4-), S2O2(3-) have no spectral effect on the 521-pigment. Cations appear to have no influence on the pigment absorption and Class I anions prevent or reverse the hypsochromic shift caused by Class II anions. It is suggested that the spectral displacements reflect specific changes in the opsin conformation, which alter the immediate (dipolar) environment of the retinal chromophore. The protein conformation seems to promote excited-state processes most in the native 521-pigment state and least in the presence of Class II anions. This in turn suggests that the photosensitivity of the 521-pigment is controlled by the excited rather than by the ground-state properties of the pigment.

Microbial Production of Food Grade Pigments

Directory of Open Access Journals (Sweden)

Laurent Dufossé

2006-01-01

Full Text Available The controversial topic of synthetic dyes in food has been discussed for many years. The scrutiny and negative assessment of synthetic food dyes by the modern consumer have raised a strong interest in natural colouring alternatives. Nature is rich in colours (minerals, plants, microalgae, etc., and pigment-producing microorganisms (fungi, yeasts, bacteria are quite common. Among the molecules produced by microorganisms are carotenoids, melanins, flavins, quinones, and more specifically monascins, violacein or indigo. The success of any pigment produced by fermentation depends upon its acceptability on the market, regulatory approval, and the size of the capital investment required to bring the product to market. A few years ago, some expressed doubts about the successful commercialization of fermentation-derived food grade pigments because of the high capital investment requirements for fermentation facilities and the extensive and lengthy toxicity studies required by regulatory agencies. Public perception of biotechnology-derived products also had to be taken into account. Nowadays some fermentative food grade pigments are on the market: Monascus pigments, astaxanthin from Xanthophyllomyces dendrorhous, Arpink Red from Penicillium oxalicum, riboflavin from Ashbya gossypii, b-carotene from Blakeslea trispora. The successful marketing of pigments derived from algae or extracted from plants, both as a food colour and a nutritional supplement, reflects the presence and importance of niche markets in which consumers are willing to pay a premium for »all natural ingredients«.

Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data

Science.gov (United States)

Kong, Weiping; Huang, Wenjiang; Zhou, Xianfeng; Song, Xiaoyu; Casa, Raffaele

2016-04-01

Precise estimation of carotenoids (Car) content in plants, from remotely sensed data, is challenging due to their small proportion in the overall total pigment content and to the overlapping of spectral absorption features with chlorophyll (Chl) in the blue region of the spectrum. The use of narrow band vegetation indices (VIs) obtained from hyperspectral data has been considered an effective way to estimate Car content. However, VIs have proved to lack sensitivity to low or high Car content in a number of studies. In this study, the carotenoid triangle ratio index (CTRI), derived from the existing modified triangular vegetation index and a single band reflectance at 531 nm, was proposed and employed to estimate Car canopy content. We tested the potential of three categories of hyperspectral indices earlier proposed for Car, Chl, Car/Chl ratio estimation, and the new CTRI index, for Car canopy content assessment in winter wheat and corn. Spectral reflectance representing plant canopies were simulated using the PROSPECT and SAIL radiative transfer model, with the aim of analyzing saturation effects of these indices, as well as Chl effects on the relationship between spectral indices and Car content. The result showed that the majority of the spectral indices tested, saturated with the increase of Car canopy content above 28 to 64 μg/cm2. Conversely, the CTRI index was more robust and was linearly and highly sensitive to Car content in winter wheat and corn datasets, with coefficients of determination of 0.92 and 0.75, respectively. The corresponding root mean square error of prediction were 6.01 and 9.70 μg/cm2, respectively. Furthermore, the CTRI index did not show a saturation effect and was not greatly influenced by changes of Chl values, outperforming all the other indices tested. Estimation of Car canopy content using the CTRI index provides an insight into diagnosing plant physiological status and environmental stress.

Innovative Hyperspectral Imaging-Based Techniques for Quality Evaluation of Fruits and Vegetables: A Review

Directory of Open Access Journals (Sweden)

Yuzhen Lu

2017-02-01

Full Text Available New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are described, followed by the mathematical methods for processing and extracting critical information from the acquired data. Applications of these techniques for property and quality evaluation of fruits and vegetables are then presented. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.

A new hyperspectral image compression paradigm based on fusion

Science.gov (United States)

Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto

2016-10-01

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor. Hence, this process must be performed by space qualified hardware, having area, power and speed limitations. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompress image. In this manuscript we proposed a new methodology for compressing hyperspectral images based on hyperspectral image fusion concepts. The proposed compression process has two independent steps. The first one is to spatially degrade the remote sensed hyperspectral image to obtain a low resolution hyperspectral image. The second step is to spectrally degrade the remote sensed hyperspectral image to obtain a high resolution multispectral image. These two degraded images are then send to the earth surface, where they must be fused using a fusion algorithm for hyperspectral and multispectral image, in order to recover the remote sensed hyperspectral image. The main advantage of the proposed methodology for compressing remote sensed hyperspectral images is that the compression process, which must be performed on-board, becomes very simple, being the fusion process used to reconstruct image the more complex one. An extra advantage is that the compression ratio can be fixed in advanced. Many simulations have been performed using different fusion algorithms and different methodologies for degrading the hyperspectral image. The results obtained in the simulations performed corroborate the benefits of the proposed methodology.

Advances in hyperspectral remote sensing I: The visible Fourier transform hyperspectral imager

Directory of Open Access Journals (Sweden)

J. Bruce Rafert

2015-05-01

Full Text Available We discuss early hyperspectral research and development activities during the 1990s that led to the deployment of aircraft and satellite payloads whose heritage was based on the use of visible, spatially modulated, imaging Fourier transform spectrometers, beginning with early experiments at the Florida Institute of Technology, through successful launch and deployment of the Visible Fourier Transform Hyperspectral Imager on MightySat II.1 on 19 July 2000. In addition to a brief chronological overview, we also discuss several of the most interesting optical engineering challenges that were addressed over this timeframe, present some as-yet un-exploited features of field-widened (slit-less SMIFTS instruments, and present some images from ground-based, aircraft-based and satellite-based instruments that helped provide the impetus for the proliferation and development of entire new families of instruments and countless new applications for hyperspectral imaging.

Connecting landscape function to hyperspectral reflectance in a dry sub-humid native grassland in southern Queensland, Australia

Science.gov (United States)

Williams, Wendy; Apan, Armando; Alchin, Bruce

2016-04-01

Native grasslands cover over 80% of significant ecosystems in Australia, stretching across arid, semi-arid, tropical, sub-tropical and savannah landscapes. Scales of pastoral operations in Australia range from hundreds of hectares to thousands of square kilometres and are predominately found in regions with highly variable rainfall. Land use is governed by the need to cope with droughts, floods and fires. Resilience to climatic extremes can be attained through effective soil management. Connecting landscape function on the fine scale to broad land management objectives is a critical step in evaluation and requires an understanding of the relevant spectral properties in remotely sensed images. The aim of this study was to assess key landscape function indices across spatial scales in order to examine their correlation with hyperspectral reflectance measurements. The results from this study could be applied as a model for land management centred on remote sensing. The study site is located at Stonehenge (southern Queensland) on a moderately deep texture contrast soil with hard setting gravelly topsoil. Mean annual rainfall of 667 mm supports open forest and native perennial pastures with a diverse biocrust dominated by N-fixing cyanobacteria. Land use history is continuous grazing however; it had been destocked for several years prior to our study. There was some evidence of cattle, kangaroos and feral herbivores (rabbits, deer and goats) although impacts appeared to be minimal. We established four land cover types: native pasture - NP1 (~100% FPC - foliage projective cover), native pasture - NP2 (~50% FPC, 50% biocrust), natural bare soil - BC (>80% biocrust), bare and eroded soil - BE (<1% biocrust). Duplicate 0.25 m2 quadrats of each land cover type were selected contiguous with a 100 m transect across the slope. The quadrats were analysed as five micro-transects with each row consisting of five sub-cells. Stability, infiltration and nutrient cycling indices were

Potential of near-infrared hyperspectral reflectance imaging for screening of farm feed contamination

Science.gov (United States)

Wang, Wenbo; Paliwal, Jitendra

2005-09-01

With the outbreak of Bovine Spongiform Encephalopathy (BSE) (commonly known as mad cow disease) in 1987 in the United Kingdom and a recent case discovered in Alberta, more and more emphasis is placed on food and farm feed quality and safety issues internationally. The disease is believed to be spread through farm feed contamination by animal byproducts in the form of meat-and-bone-meal (MBM). The paper reviewed the available techniques necessary to the enforcement of legislation concerning the feed safety issues. The standard microscopy method, although highly sensitive, is laborious and costly. A method to routinely screen farm feed contamination certainly helps to reduce the complexity of safety inspection. A hyperspectral imaging system working in the near-infrared wavelength region of 1100-1600 nm was used to study the possibility of detection of ground broiler feed contamination by ground pork. Hyperspectral images of raw broiler feed, ground broiler feed, ground pork, and contaminated feed samples were acquired. Raw broiler feed samples were found to possess comparatively large spectral variations due to light scattering effect. Ground feed adulterated with 1%, 3%, 5%, and 10% of ground pork was tested to identify feed contamination. Discriminant analysis using Mahalanobis distance showed that the model trained using pure ground feed samples and pure ground pork samples resulted in 100% false negative errors for all test replicates of contaminated samples. A discriminant model trained with pure ground feed samples and 10% contamination level samples resulted in 12.5% false positive error and 0% false negative error.

Multiband and Lossless Compression of Hyperspectral Images

Directory of Open Access Journals (Sweden)

Raffaele Pizzolante

2016-02-01

Full Text Available Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.. We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.

Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark

Directory of Open Access Journals (Sweden)

Zebin Wu

2016-01-01

Full Text Available Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS, and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.

Galvanic displacement synthesis of Al/Ni core–shell pigments and their low infrared emissivity application

Energy Technology Data Exchange (ETDEWEB)

Yuan, Le, E-mail: yuanle.cn@gmail.com [Center for Advanced Materials and Energy, Xihua University, Chengdu, 610039 (China); National Engineering Research Center of Electromagnetic Radiation Control Materials, UESTC, Chengdu, 610054 (China); Hu, Juan [Center for Advanced Materials and Energy, Xihua University, Chengdu, 610039 (China); Weng, Xiaolong [National Engineering Research Center of Electromagnetic Radiation Control Materials, UESTC, Chengdu, 610054 (China); Zhang, Qingyong [Center for Advanced Materials and Energy, Xihua University, Chengdu, 610039 (China); Deng, Longjiang [National Engineering Research Center of Electromagnetic Radiation Control Materials, UESTC, Chengdu, 610054 (China)

2016-06-15

We have successfully developed a magnetic Al/Ni core–shell pigment via a galvanic displacement reaction to obtain low infrared emissivity pigment with low lightness and visible light reflectance. Al/Ni core–shell particles were prepared via a simple one-step synthetic method where Ni was deposited onto the Al surface at the expense of Al atoms. The influence of pH and the amount of NH{sub 4}F complexing agent on phase structure, surface morphology, optical and magnetic properties were studied systematically. The neutral condition and high concentration of NH{sub 4}F forms smooth, flat, uniform and dense Ni shell on the surface of flake Al particles, which can significantly reduce the lightness and visible light reflectance but slightly increase the infrared emissivity. When the core–shell pigments are prepared in neutral pH solution at NH{sub 4}F = 11.2 g/L, the lightness (L{sup *}) and visual light reflectivity can be reduced by 12.6 and 0.46, respectively versus uncoated flake Al pigments, but the infrared emissivity is only increased by 0.02. The color changes from brilliant silver to gray black and the saturation magnetization value is 6.59 emu/g. Therefore, these Al/Ni magnetic composite pigments can be used as a novel low infrared emissivity pigment to improve the multispectral stealth performance of low-E coatings in the visual, IR and Radar wavebands. - Highlights: • Prepared magnetic Al/Ni core–shell pigment with low lightness and low emissivity. • Used one-pot galvanic displacement reaction to form smooth and dense Ni shell. • Show enhanced stealth performance in the visual, IR and Radar wavebands. • The lightness and visible light reflectance was decreased by 12.6 and 0.46. • But the infrared emissivity was only increases by 0.02.

Galvanic displacement synthesis of Al/Ni core–shell pigments and their low infrared emissivity application

International Nuclear Information System (INIS)

Yuan, Le; Hu, Juan; Weng, Xiaolong; Zhang, Qingyong; Deng, Longjiang

2016-01-01

We have successfully developed a magnetic Al/Ni core–shell pigment via a galvanic displacement reaction to obtain low infrared emissivity pigment with low lightness and visible light reflectance. Al/Ni core–shell particles were prepared via a simple one-step synthetic method where Ni was deposited onto the Al surface at the expense of Al atoms. The influence of pH and the amount of NH_4F complexing agent on phase structure, surface morphology, optical and magnetic properties were studied systematically. The neutral condition and high concentration of NH_4F forms smooth, flat, uniform and dense Ni shell on the surface of flake Al particles, which can significantly reduce the lightness and visible light reflectance but slightly increase the infrared emissivity. When the core–shell pigments are prepared in neutral pH solution at NH_4F = 11.2 g/L, the lightness (L"*) and visual light reflectivity can be reduced by 12.6 and 0.46, respectively versus uncoated flake Al pigments, but the infrared emissivity is only increased by 0.02. The color changes from brilliant silver to gray black and the saturation magnetization value is 6.59 emu/g. Therefore, these Al/Ni magnetic composite pigments can be used as a novel low infrared emissivity pigment to improve the multispectral stealth performance of low-E coatings in the visual, IR and Radar wavebands. - Highlights: • Prepared magnetic Al/Ni core–shell pigment with low lightness and low emissivity. • Used one-pot galvanic displacement reaction to form smooth and dense Ni shell. • Show enhanced stealth performance in the visual, IR and Radar wavebands. • The lightness and visible light reflectance was decreased by 12.6 and 0.46. • But the infrared emissivity was only increases by 0.02.

[Analysis of sensitive spectral bands for burning status detection using hyper-spectral images of Tiangong-01].

Science.gov (United States)

Qin, Xian-Lin; Zhu, Xi; Yang, Fei; Zhao, Kai-Rui; Pang, Yong; Li, Zeng-Yuan; Li, Xu-Zhi; Zhang, Jiu-Xing

2013-07-01

To obtain the sensitive spectral bands for detection of information on 4 kinds of burning status, i. e. flaming, smoldering, smoke, and fire scar, with satellite data, analysis was conducted to identify suitable satellite spectral bands for detection of information on these 4 kinds of burning status by using hyper-spectrum images of Tiangong-01 (TG-01) and employing a method combining statistics and spectral analysis. The results show that: in the hyper-spectral images of TG-01, the spectral bands differ obviously for detection of these 4 kinds of burning status; in all hyper-spectral short-wave infrared channels, the reflectance of flaming is higher than that of all other 3 kinds of burning status, and the reflectance of smoke is the lowest; the reflectance of smoke is higher than that of all other 3 kinds of burning status in the channels corresponding to hyper-spectral visible near-infrared and panchromatic sensors. For spectral band selection, more suitable spectral bands for flaming detection are 1 000.0-1 956.0 and 2 020.0-2 400.0 nm; the suitable spectral bands for identifying smoldering are 930.0-1 000.0 and 1 084.0-2 400.0 nm; the suitable spectral bands for smoke detection is in 400.0-920.0 nm; for fire scar detection, it is suitable to select bands with central wavelengths of 900.0-930.0 and 1 300.0-2 400.0 nm, and then to combine them to construct a detection model.

Kingfisher feathers--colouration by pigments, spongy nanostructures and thin films.

Science.gov (United States)

Stavenga, Doekele G; Tinbergen, Jan; Leertouwer, Hein L; Wilts, Bodo D

2011-12-01

The colours of the common kingfisher, Alcedo atthis, reside in the barbs of the three main types of feather: the orange breast feathers, the cyan back feathers and the blue tail feathers. Scanning electron microscopy showed that the orange barbs contain small pigment granules. The cyan and blue barbs contain spongy nanostructures with slightly different dimensions, causing different reflectance spectra. Imaging scatterometry showed that the pigmented barbs create a diffuse orange scattering and the spongy barb structures create iridescence. The extent of the angle-dependent light scattering increases with decreasing wavelength. All barbs have a cortical envelope with a thickness of a few micrometres. The reflectance spectra of the cortex of the barbs show oscillations when measured from small areas, but when measured from larger areas the spectra become wavelength independent. This can be directly understood with thin film modelling, assuming a somewhat variable cortex thickness. The cortex reflectance appears to be small but not negligible with respect to the pigmentary and structural barb reflectance.

Color of Cultures of Staphylococcus epidermidis Determined by Spectral Reflectance Colorimetry

Science.gov (United States)

Brown, Richard W.

1966-01-01

Brown, Richard W. (National Animal Disease Laboratory, Ames, Iowa). Color of cultures of Staphylococcus epidermidis determined by spectral reflectance colorimetry. J. Bacteriol. 91:911–918. 1966.—A colorimeter with a reflectance attachment was used to study pigment production by Staphylococcus epidermidis strains grown on a medium containing Trypticase Soy Agar (BBL) and cream. The color of each culture was first characterized by reflectance colorimetry for dominant wavelength, purity, and luminous reflectance (Y) and was then classified visually into 1 of 10 color grades. There was not complete agreement in grading colors by the two methods, inasmuch as cultures that were considered more pigmented in relation to other cultures by the reflectance method were sometimes graded visually as less pigmented, and vice versa. Nevertheless, when the cultures were visually graded as being more pigmented, there was a concomitant increase in the average values of dominant wavelength and purity with a decrease in Y for the cultures in each higher grade. Thus, the nonpigmented cultures had the lowest dominant wavelength and purity values but the highest Y (brightness) values, whereas the most pigmented cultures had the highest dominant wavelength and purity values, but the lowest Y values. These results indicated that the cultures did not produce pigments of different hues (greenish-yellow, yellow, yellowish-orange) each with high, medium, and low degrees of purity and brightness. The value (1 − z), where the chromaticity coordinate z = Z/(X + Y + Z), was found to be proportional to the purity value. An inverse relationship between the tristimulus Z and purity values was also demonstrated. All cultures tested by the reflectance method were also classified according to the type of spectral absorption curve obtained with pigments extracted from the cultures with methanol. A comparison of these methods indicated that determining the type of spectral absorption curve would be

Spectral discrimination of macrophyte species during different seasons in a tropical wetland using in-situ hyperspectral remote sensing

Science.gov (United States)

Saluja, Ridhi; Garg, J. K.

2017-10-01

Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

A BAND SELECTION METHOD FOR SUB-PIXEL TARGET DETECTION IN HYPERSPECTRAL IMAGES BASED ON LABORATORY AND FIELD REFLECTANCE SPECTRAL COMPARISON

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S. Sharifi hashjin

2016-06-01

Full Text Available In recent years, developing target detection algorithms has received growing interest in hyperspectral images. In comparison to the classification field, few studies have been done on dimension reduction or band selection for target detection in hyperspectral images. This study presents a simple method to remove bad bands from the images in a supervised manner for sub-pixel target detection. The proposed method is based on comparing field and laboratory spectra of the target of interest for detecting bad bands. For evaluation, the target detection blind test dataset is used in this study. Experimental results show that the proposed method can improve efficiency of the two well-known target detection methods, ACE and CEM.

Removal of clouds, dust and shadow pixels from hyperspectral imagery using a non-separable and stationary spatio-temporal covariance model

KAUST Repository

Angel, Yoseline; Houborg, Rasmus; McCabe, Matthew

2016-01-01

Hyperspectral remote sensing images are usually affected by atmospheric conditions such as clouds and their shadows, which represents a contamination of reflectance data and complicates the extraction of biophysical variables to monitor phenological

Hyperspectral optical tomography of intrinsic signals in the rat cortex

Science.gov (United States)

Konecky, Soren D.; Wilson, Robert H.; Hagen, Nathan; Mazhar, Amaan; Tkaczyk, Tomasz S.; Frostig, Ron D.; Tromberg, Bruce J.

2015-01-01

Abstract. We introduce a tomographic approach for three-dimensional imaging of evoked hemodynamic activity, using broadband illumination and diffuse optical tomography (DOT) image reconstruction. Changes in diffuse reflectance in the rat somatosensory cortex due to stimulation of a single whisker were imaged at a frame rate of 5 Hz using a hyperspectral image mapping spectrometer. In each frame, images in 38 wavelength bands from 484 to 652 nm were acquired simultaneously. For data analysis, we developed a hyperspectral DOT algorithm that used the Rytov approximation to quantify changes in tissue concentration of oxyhemoglobin (ctHbO2) and deoxyhemoglobin (ctHb) in three dimensions. Using this algorithm, the maximum changes in ctHbO2 and ctHb were found to occur at 0.29±0.02 and 0.66±0.04  mm beneath the surface of the cortex, respectively. Rytov tomographic reconstructions revealed maximal spatially localized increases and decreases in ctHbO2 and ctHb of 321±53 and 555±96  nM, respectively, with these maximum changes occurring at 4±0.2  s poststimulus. The localized optical signals from the Rytov approximation were greater than those from modified Beer–Lambert, likely due in part to the inability of planar reflectance to account for partial volume effects. PMID:26835483

Controlling the radiative properties of cool black-color coatings pigmented with CuO submicron particles

International Nuclear Information System (INIS)

Gonome, Hiroki; Baneshi, Mehdi; Okajima, Junnosuke; Komiya, Atsuki; Maruyama, Shigenao

2014-01-01

The objective of this study was to design a pigmented coating with dark appearance that maintains a low temperature while exposed to sunlight. The radiative properties of a black-color coating pigmented with copper oxide (CuO) submicron particles are described. In the present work, the spectral behavior of the CuO-pigmented coating was calculated. The radiative properties of CuO particles were evaluated, and the radiative transfer in the pigmented coating was modeled using the radiation element method by ray emission model (REM 2 ). The coating is made using optimized particles. The reflectivity is measured by spectroscopy and an integrating sphere in the visible (VIS) and near infrared (NIR) regions. By using CuO particles controlled in size, we were able to design a black-color coating with high reflectance in the NIR region. The coating substrate also plays an important role in controlling the reflectance. The NIR reflectance of the coating on a standard white substrate with appropriate coating thickness and volume fraction was much higher than that on a standard black substrate. From the comparison between the experimental and calculated results, we know that more accurate particle size control enables us to achieve better performance. The use of appropriate particles with optimum size, coating thickness and volume fraction on a suitable substrate enables cool and black-color coating against solar irradiation. -- Highlights: • A new approach in designing pigmented coatings was used. • The effects of particles size on both visible and near infrared reflectivities were studied. • The results of numerical calculation were compared with experimental ones for CuO powders

Kingfisher feathers - colouration by pigments, spongy nanostructures and thin films

OpenAIRE

Stavenga, Doekele G.; Tinbergen, Jan; Leertouwer, Hein L.; Wilts, Bodo D.

2011-01-01

The colours of the common kingfisher, Alcedo atthis, reside in the barbs of the three main types of feather: the orange breast feathers, the cyan back feathers and the blue tail feathers. Scanning electron microscopy showed that the orange barbs contain small pigment granules. The cyan and blue barbs contain spongy nanostructures with slightly different dimensions, causing different reflectance spectra. Imaging scatterometry showed that the pigmented barbs create a diffuse orange scattering a...

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging.

Science.gov (United States)

Chu, Bingquan; Yu, Keqiang; Zhao, Yanru; He, Yong

2018-04-19

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

Science.gov (United States)

Chu, Bingquan; Yu, Keqiang; Zhao, Yanru

2018-01-01

This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874–1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans. PMID:29671781

Development of Noninvasive Classification Methods for Different Roasting Degrees of Coffee Beans Using Hyperspectral Imaging

Directory of Open Access Journals (Sweden)

Bingquan Chu

2018-04-01

Full Text Available This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI. The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874–1734 nm were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

The study of active tectonic based on hyperspectral remote sensing

Science.gov (United States)

Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

2017-12-01

As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm

Detection of Isoflavones Content in Soybean Based on Hyperspectral Imaging Technology

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Tan Kezhu

2014-04-01

Full Text Available Because of many important biological activities, Soybean isoflavones which has great potential for exploitation is significant to practical applications. Due to the conventional methods for determination of soybean isoflavones having long detection period, used too many reagents, couldn’t be detected on-line, and other issues, we propose hyperspectral imaging technology to detect the contents of soybean isoflavones. Based on the 40 varieties of soybeans produced in Heilongjiang province, we get the spectral reflection datum of soybean samples varied from the soybean’s hyperspectral images which are collected by the hyperspectral imaging system, and apply high performance liquid chromatography (HPLC method to determine the true value of the selected samples of isoflavones. The feature wavelengths for isoflavones content prediction (1516, 1572, 1691, 1716 and 1760 nm were selected based on correlation analysis. The prediction model was established by using the method of BP neural network in order to realize the prediction of soybean isoflavones content analysis. The experimental results show that, the ANN model could predict isoflavones content of soybean samples with of 0.9679, the average relative error is 0.8032 %, and the mean square error (MSE is 0.110328, which indicates the effectiveness of the proposed method and provides a theoretical basis for the applications of hyerspectral imaging in non-destructive detection for interior quality of soybean.

Detecting in-field variation in photosynthetic capacity of trangenically modifed plants with hyperspectral imaging.

Science.gov (United States)

Meacham, K.; Montes, C.; Pederson, T.; Wu, J.; Guan, K.; Bernacchi, C.

2017-12-01

Improved photosynthetic rates have been shown to increase crop biomass, making improved photosynthesis a focus for driving future grain yield increases. Improving the photosynthetic pathway offers opportunity to meet food demand, but requires high throughput measurement techniques to detect photosynthetic variation in natural accessions and transgenically modified plants. Gas exchange measurements are the most widely used method of measuring photosynthesis in field trials but this process is laborious and slow, and requires further modeling to estimate meaningful parameters and to upscale to the plot or canopy level. In field trials of tobacco with modifications made to the photosynthetic pathway, we infer the maximum carboxylation rate of Rubisco (Vcmax) and maximum electron transport rate (Jmax) and detect photosynthetic variation from hyperspectral imaging with a partial least squares regression technique. Ground-truth measurements from photosynthetic gas exchange, a full-range (400-2500nm) handheld spectroadiometer with leaf clip, hyperspectral indices, and extractions of leaf pigments support the model. The results from a range of wild-type cultivars and from genetically modified germplasm suggest that the opportunity for rapid selection of top performing genotypes from among thousands of plots. This research creates the opportunity to extend agroecosystem models from simplified "one-cultivar" generic parameterization to better represent a full suite of current and future crop cultivars for a wider range of environmental conditions.

Development of paints with infrared radiation reflective properties

Directory of Open Access Journals (Sweden)

Eliane Coser

2015-06-01

Full Text Available AbstractLarge buildings situated in hot regions of the Globe need to be agreeable to their residents. Air conditioning is extensively used to make these buildings comfortable, with consequent energy consumption. Absorption of solar visible and infrared radiations are responsible for heating objects on the surface of the Earth, including houses and buildings. To avoid excessive energy consumption, it is possible to use coatings formulated with special pigments that are able to reflect the radiation in the near- infrared, NIR, spectrum. To evaluate this phenomenon an experimental study about the reflectivity of paints containing infrared-reflective pigments has been made. By irradiating with an IR source and by measuring the surface temperatures of the samples we evaluated: color according to ASTM D 2244-14, UV/VIS/NIR reflectance according to ASTM E 903-12 and thermal performance. Additionally, the spectral reflectance and the IR emittance were measured and the solar reflectance of the samples were calculated. The results showed that plates coated with paints containing IR-reflecting pigments displayed lower air temperature on the opposite side as compared to conventional coatings, indicating that they can be effective to reflect NIR and decrease the temperature of buildings when used in roofs and walls.

Using hyperspectral imaging technology to identify diseased tomato leaves

Science.gov (United States)

Li, Cuiling; Wang, Xiu; Zhao, Xueguan; Meng, Zhijun; Zou, Wei

2016-11-01

In the process of tomato plants growth, due to the effect of plants genetic factors, poor environment factors, or disoperation of parasites, there will generate a series of unusual symptoms on tomato plants from physiology, organization structure and external form, as a result, they cannot grow normally, and further to influence the tomato yield and economic benefits. Hyperspectral image usually has high spectral resolution, not only contains spectral information, but also contains the image information, so this study adopted hyperspectral imaging technology to identify diseased tomato leaves, and developed a simple hyperspectral imaging system, including a halogen lamp light source unit, a hyperspectral image acquisition unit and a data processing unit. Spectrometer detection wavelength ranged from 400nm to 1000nm. After hyperspectral images of tomato leaves being captured, it was needed to calibrate hyperspectral images. This research used spectrum angle matching method and spectral red edge parameters discriminant method respectively to identify diseased tomato leaves. Using spectral red edge parameters discriminant method produced higher recognition accuracy, the accuracy was higher than 90%. Research results have shown that using hyperspectral imaging technology to identify diseased tomato leaves is feasible, and provides the discriminant basis for subsequent disease control of tomato plants.

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

Directory of Open Access Journals (Sweden)

Chunyang Wang

2015-01-01

Full Text Available Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

Reflectance variability of surface coatings reveals characteristic eigenvalue spectra

Science.gov (United States)

Medina, José M.; Díaz, José A.; Barros, Rui

2012-10-01

We have examined the trial-to-trial variability of the reflectance spectra of surface coatings containing effect pigments. Principal component analysis of reflectances was done at each detection angle separately. A method for classification of principal components is applied based on the eigenvalue spectra. It was found that the eigenvalue spectra follow characteristic power laws and depend on the detection angle. Three different subsets of principal components were examined to separate the relevant spectral features related to the pigments from other noise sources. Reconstruction of the reflectance spectra by taking only the first subset indicated that reflectance variability was higher at near-specular reflection, suggesting a correlation with the trial-to-trial deposition of effect pigments. Reconstruction by using the second subset indicates that variability was higher at short wavelengths. Finally, reconstruction by using only the third subset indicates that reflectance variability was not totally random as a function of the wavelength. The methods employed can be useful in the evaluation of color variability in industrial paint application processes.

Detection of environmental change using hyperspectral remote sensing at Olkiluoto repository site

International Nuclear Information System (INIS)

Tuominen, J.; Lipping, T.

2011-03-01

In this report methods related to hyperspectral monitoring of Olkiluoto repository site are described. A short introduction to environmental remote sensing is presented, followed by more detailed description of hyperspectral imaging and a review of applications of hyperspectral remote sensing presented in the literature. The trends of future hyperspectral imaging are discussed exploring the possibilities of long-wave infrared hyperspectral imaging. A detailed description of HYPE08 hyperspectral flight campaign at the Olkiluoto region in 2008 is presented. In addition, related pre-processing and atmospheric correction methods, necessary in monitoring use, and the quality control methods applied, are described. Various change detection methods presented in the literature are described, too. Finally, a system for hyperspectral monitoring is proposed. The system is based on continued hyperspectral airborne flight campaigns and precisely defined data processing procedure. (orig.)

Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data

Directory of Open Access Journals (Sweden)

A. Sadeghi

2012-11-01

Full Text Available The goal of this study was to improve PhytoDOAS, which is a new retrieval method for quantitative identification of major phytoplankton functional types (PFTs using hyper-spectral satellite data. PhytoDOAS is an extension of the Differential Optical Absorption Spectroscopy (DOAS, a method for detection of atmospheric trace gases, developed for remote identification of oceanic phytoplankton groups. Thus far, PhytoDOAS has been successfully exploited to identify cyanobacteria and diatoms over the global ocean from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY hyper-spectral data. This study aimed to improve PhytoDOAS for remote identification of coccolithophores, another functional group of phytoplankton. The main challenge for retrieving more PFTs by PhytoDOAS is to overcome the correlation effects between different PFT absorption spectra. Different PFTs are composed of different types and amounts of pigments, but also have pigments in common, e.g. chl a, causing correlation effects in the usual performance of the PhytoDOAS retrieval. Two ideas have been implemented to improve PhytoDOAS for the PFT retrieval of more phytoplankton groups. Firstly, using the fourth-derivative spectroscopy, the peak positions of the main pigment components in each absorption spectrum have been derived. After comparing the corresponding results of major PFTs, the optimized fit-window for the PhytoDOAS retrieval of each PFT was determined. Secondly, based on the results from derivative spectroscopy, a simultaneous fit of PhytoDOAS has been proposed and tested for a selected set of PFTs (coccolithophores, diatoms and dinoflagellates within an optimized fit-window, proven by spectral orthogonality tests. The method was then applied to the processing of SCIAMACHY data over the year 2005. Comparisons of the PhytoDOAS coccolithophore retrievals in 2005 with other coccolithophore-related data showed similar patterns in their

Colors and pterin pigmentation of pierid butterfly wings

NARCIS (Netherlands)

Wijnen, B.; Leertouwer, H. L.; Stavenga, D. G.

2007-01-01

The reflectance of pierid butterfly wings is principally determined by the incoherent scattering of incident light and the absorption by pterin pigments in the scale structures. Coherent scattering causing iridescence is frequently encountered in the dorsal wings or wing tips of male pierids. We

Genetic and Virulent Difference Between Pigmented and Non-pigmented Staphylococcus aureus

OpenAIRE

Jing Zhang; Yujuan Suo; Daofeng Zhang; Fangning Jin; Hang Zhao; Chunlei Shi

2018-01-01

Staphyloxanthin (STX), a golden carotenoid pigment produced by Staphylococcus aureus, is suggested to act as an important virulence factor due to its antioxidant properties. Restraining biosynthesis of STX was considered as an indicator of virulence decline in pigmented S. aureus isolates. However, it is not clear whether natural non-pigmented S. aureus isolates have less virulence than pigmented ones. In this study, it is aimed to compare the pigmented and non-pigmented S. aureus isolates to...

Real-time progressive hyperspectral image processing endmember finding and anomaly detection

CERN Document Server

Chang, Chein-I

2016-01-01

The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book. Includes preliminary background which is essential to those who work in hyperspectral ima...

Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

Science.gov (United States)

Arulbalaji, Palanisamy; Balasubramanian, Gurugnanam

2017-07-01

This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

The hyperspectral imaging trade-off

DEFF Research Database (Denmark)

Carstensen, Jens Michael

, this will be the standard situation, and it enables the detection of small spectral features like peaks, valleys and shoulders for a wide range of chemistries. Everything else being equal this is what you would wish for, and hyperspectral imaging is often used in research and in remote sensing because of the needs and cost......Although it has no clear-cut definition, hyperspectral imaging in the UV-Visible-NIR wavelength region seems to mean spectral image sampling in bands from 10 nm width or narrower that enables spectral reconstruction over some wavelength interval. For non-imaging spectral applications...... structures in these projects. However, hyperspectral imaging is a sampling choice within spectral imaging that typically will impose some trade-offs, and these trade-offs will not be optimal for many applications. The purpose of this presentation is to point out and increase the awareness of these trade...

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

Science.gov (United States)

Houborg, Rasmus; McCabe, Matthew F.; Angel, Yoseline; Middleton, Elizabeth M.

2016-01-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Hyperspectral imaging of the microscale distribution and dynamics of microphytobenthos in intertidal sediments

KAUST Repository

Chennu, Arjun; Fä rber, Paul; Volkenborn, Nils; Alnajjar, Mohammad Ahmad; Janssen, Felix; de Beer, Dirk; Polerecky, Lubos

2013-01-01

We describe a novel, field-deployable hyperspectral imaging system, called Hypersub, that allows noninvasive in situ mapping of the microphytobenthos (MPB) biomass distribution with a high spatial (sub-millimeter) and temporal (minutes) resolution over areas of 1 × 1 m. The biomass is derived from a log-transformed and near-infrared corrected reflectance hyperspectral index, which exhibits a linear relationship (R2 > 0.97) with the chlorophyll a (Chl a) concentration in the euphotic zone of the sediment and depends on the sediment grain size. Deployments of the system revealed that due to factors such as sediment topography, bioturbation, and grazing, the distribution of MPB in intertidal sediments is remarkably heterogeneous, with Chl a concentrations varying laterally by up to 400% of the average value over a distance of 1 cm. Furthermore, due to tidal cycling and diel light variability, MPB concentrations in the top 1 mm of sediments are very dynamic, changing by 40–80% over a few hours due to vertical migration. We argue that the high-resolution hyperspectral imaging method overcomes the inadequate resolution of traditional methods based on sedimentary Chl a extraction, and thus helps improve our understanding of the processes that control benthic primary production in coastal sediments.

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

KAUST Repository

Houborg, Rasmus; McCabe, Matthew; Angel, Yoseline; Middleton, Elizabeth M.

2016-01-01

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Hyperspectral imaging of the microscale distribution and dynamics of microphytobenthos in intertidal sediments

KAUST Repository

Chennu, Arjun

2013-10-03

We describe a novel, field-deployable hyperspectral imaging system, called Hypersub, that allows noninvasive in situ mapping of the microphytobenthos (MPB) biomass distribution with a high spatial (sub-millimeter) and temporal (minutes) resolution over areas of 1 × 1 m. The biomass is derived from a log-transformed and near-infrared corrected reflectance hyperspectral index, which exhibits a linear relationship (R2 > 0.97) with the chlorophyll a (Chl a) concentration in the euphotic zone of the sediment and depends on the sediment grain size. Deployments of the system revealed that due to factors such as sediment topography, bioturbation, and grazing, the distribution of MPB in intertidal sediments is remarkably heterogeneous, with Chl a concentrations varying laterally by up to 400% of the average value over a distance of 1 cm. Furthermore, due to tidal cycling and diel light variability, MPB concentrations in the top 1 mm of sediments are very dynamic, changing by 40–80% over a few hours due to vertical migration. We argue that the high-resolution hyperspectral imaging method overcomes the inadequate resolution of traditional methods based on sedimentary Chl a extraction, and thus helps improve our understanding of the processes that control benthic primary production in coastal sediments.

Detection of chlorophyll and leaf area index dynamics from sub-weekly hyperspectral imagery

KAUST Repository

Houborg, Rasmus

2016-10-25

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense timeseries of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Structural and Visible-Near Infrared Optical Properties of Cr-Doped TiO2 for Colored Cool Pigments

Science.gov (United States)

Yuan, Le; Weng, Xiaolong; Zhou, Ming; Zhang, Qingyong; Deng, Longjiang

2017-11-01

Chromium-doped TiO2 pigments were synthesized via a solid-state reaction method and studied with X-ray diffraction, SEM, XPS, and UV-VIS-NIR reflectance spectroscopy. The incorporation of Cr3+ accelerates the transition from the anatase phase to the rutile phase and compresses the crystal lattice. Moreover, the particle morphology, energy gap, and reflectance spectrum of Cr-doped TiO2 pigments is affected by the crystal structure and doping concentration. For the rutile samples, some of the Cr3+ ions are oxidized to Cr4+ after sintering at a high temperature, which leads to a strong near-infrared absorption band due to the 3A2 → 3 T1 electric dipole-allowed transitions of Cr4+. And the decrease of the band gap causes an obvious redshift of the optical absorption edges as the doping concentration increases. Thus, the VIS and near-infrared average reflectance of the rutile Ti1 - x Cr x O2 sample decrease by 60.2 and 58%, respectively, when the Cr content increases to x = 0.0375. Meanwhile, the color changes to black brown. However, for the anatase Ti1 - x Cr x O2 pigments, only the VIS reflection spectrum is inhibited by forming some characteristic visible light absorption peaks of Cr3+. The morphology, band gap, and NIR reflectance are not significantly affected. Finally, a Cr-doped anatase TiO2 pigment with a brownish-yellow color and 90% near-infrared reflectance can be obtained.

Analytic Hyperspectral Sensing

National Research Council Canada - National Science Library

Coifman, Ronald R

2005-01-01

In the last year (no-cost extension), Plain Sight Systems reached the goal of successfully building its second NIR standoff hyperspectral imaging system, NSTIS, the Near-Infrared Spectral Target Identification System...

HYPERSPECTRAL HYPERION IMAGERY ANALYSIS AND ITS APPLICATION USING SPECTRAL ANALYSIS

Directory of Open Access Journals (Sweden)

W. Pervez

2015-03-01

Full Text Available Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.

Mapping Soil Organic Matter with Hyperspectral Imaging

Science.gov (United States)

Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

2014-05-01

Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

[Investigation of the hyperspectral image characteristics of wheat leaves under different stress].

Science.gov (United States)

Zhang, Dong-Yan; Zhang, Jing-Cheng; Zhu, Da-Zhou; Wang, Ji-Hua; Luo, Ju-Hua; Zhao, Jin-Ling; Huang, Wen-Jiang

2011-04-01

The diagnosis of growing status and vigor of crops under various stresses is an important step in precision agriculture. Hyperspectral imaging technology has the advantage of providing both spectral and spatial information simultaneously, and has become a research hot spot. In the present study, auto-development of the pushbroom imaging spectrometer (PIS) was utilized to collect hyperspectral images of wheat leaves which suffer from shortage of nutrient, pest and disease stress. The hyperspectral cube was processed by the method of pixel average step by step to highlight the spectral characteristics, which facilitate the analysis based on the differences of leaves reflectance. The results showed that the hyperspectra of leaves from different layers can display nutrient differences, and recognize intuitively different stress extent by imaging figures. With the 2 nanometer spectral resolution and millimeter level spatial resolution of PIS, the number of disease spot can be qualitatively calculated when crop is infected with diseases, and, the area of plant disease could also be quantitatively analyzed; when crop suffered from pest and insect, the spectral information of leaves with single aphid and aphids can be detected by PIS, which provides a new means to quantitatively detect the aphid destroying of wheat leaf. The present study demonstrated that hyperspecral imaging has a great potential in quantitative and qualitative analysis of crop growth.

Genetic and Virulent Difference Between Pigmented and Non-pigmented Staphylococcus aureus.

Science.gov (United States)

Zhang, Jing; Suo, Yujuan; Zhang, Daofeng; Jin, Fangning; Zhao, Hang; Shi, Chunlei

2018-01-01

Staphyloxanthin (STX), a golden carotenoid pigment produced by Staphylococcus aureus , is suggested to act as an important virulence factor due to its antioxidant properties. Restraining biosynthesis of STX was considered as an indicator of virulence decline in pigmented S. aureus isolates. However, it is not clear whether natural non-pigmented S. aureus isolates have less virulence than pigmented ones. In this study, it is aimed to compare the pigmented and non-pigmented S. aureus isolates to clarify the genetic and virulent differences between the two groups. Here, 132 S. aureus isolates were divided into two phenotype groups depending on the absorbance (OD 450 ) of the extracted carotenoids. Then, all isolates were subjected to spa typing and multilocus sequence typing (MLST), and then the detection of presence of 30 virulence factors and the gene integrity of crtN and crtM . Furthermore, 24 typical S. aureus isolates and 4 S. argenteus strains were selected for the murine infection assay of in vivo virulence, in which the histological observation and enumeration of CFUs were carried out. These isolates were distributed in 26 sequence types (STs) and 49 spa types. The pigmented isolates were scattered in 25 STs, while the non-pigmented isolates were more centralized, which mainly belonged to ST20 (59%) and ST25 (13%). Among the 54 non-pigmented isolates, about 20% carried intact crtN and crtM genes. The in vivo assay suggested that comparing with pigmented S. aureus , non-pigmented S. aureus and S. argenteus strains did not show a reduced virulence in murine sepsis models. Therefore, it suggested that there were no significant genetic and virulent differences between pigmented and non-pigmented S. aureus .

Environmental variables, algal pigments and phytoplankton in the ...

African Journals Online (AJOL)

The phytoplankton diversity, environmental variables and algal pigments of the Atlantic Ocean off the coast of Badagry, Lagos were investigated for twelve months between May 2015 and April 2016. The water chemistry characteristics reflected sea water conditions. At the two stations, the range of values recorded for some ...

Estimating Leaf Water Potential of Giant Sequoia Trees from Airborne Hyperspectral Imagery

Science.gov (United States)

Francis, E. J.; Asner, G. P.

2015-12-01

Recent drought-induced forest dieback events have motivated research on the mechanisms of tree survival and mortality during drought. Leaf water potential, a measure of the force exerted by the evaporation of water from the leaf surface, is an indicator of plant water stress and can help predict tree mortality in response to drought. Scientists have traditionally measured water potentials on a tree-by-tree basis, but have not been able to produce maps of tree water potential at the scale of a whole forest, leaving forest managers unaware of forest drought stress patterns and their ecosystem-level consequences. Imaging spectroscopy, a technique for remote measurement of chemical properties, has been used to successfully estimate leaf water potentials in wheat and maize crops and pinyon-pine and juniper trees, but these estimates have never been scaled to the canopy level. We used hyperspectral reflectance data collected by the Carnegie Airborne Observatory (CAO) to map leaf water potentials of giant sequoia trees (Sequoiadendron giganteum) in an 800-hectare grove in Sequoia National Park. During the current severe drought in California, we measured predawn and midday leaf water potentials of 48 giant sequoia trees, using the pressure bomb method on treetop foliage samples collected with tree-climbing techniques. The CAO collected hyperspectral reflectance data at 1-meter resolution from the same grove within 1-2 weeks of the tree-level measurements. A partial least squares regression was used to correlate reflectance data extracted from the 48 focal trees with their water potentials, producing a model that predicts water potential of giant sequoia trees. Results show that giant sequoia trees can be mapped in the imagery with a classification accuracy of 0.94, and we predicted the water potential of the mapped trees to assess 1) similarities and differences between a leaf water potential map and a canopy water content map produced from airborne hyperspectral data, 2

Research on hyperspectral dynamic scene and image sequence simulation

Science.gov (United States)

Sun, Dandan; Liu, Fang; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei

2016-10-01

This paper presents a simulation method of hyperspectral dynamic scene and image sequence for hyperspectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyperspectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyperspectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyperspectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyperspectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyperspectral images are consistent with the theoretical analysis results.

Genetic and Virulent Difference Between Pigmented and Non-pigmented Staphylococcus aureus

Directory of Open Access Journals (Sweden)

Jing Zhang

2018-04-01

Full Text Available Staphyloxanthin (STX, a golden carotenoid pigment produced by Staphylococcus aureus, is suggested to act as an important virulence factor due to its antioxidant properties. Restraining biosynthesis of STX was considered as an indicator of virulence decline in pigmented S. aureus isolates. However, it is not clear whether natural non-pigmented S. aureus isolates have less virulence than pigmented ones. In this study, it is aimed to compare the pigmented and non-pigmented S. aureus isolates to clarify the genetic and virulent differences between the two groups. Here, 132 S. aureus isolates were divided into two phenotype groups depending on the absorbance (OD450 of the extracted carotenoids. Then, all isolates were subjected to spa typing and multilocus sequence typing (MLST, and then the detection of presence of 30 virulence factors and the gene integrity of crtN and crtM. Furthermore, 24 typical S. aureus isolates and 4 S. argenteus strains were selected for the murine infection assay of in vivo virulence, in which the histological observation and enumeration of CFUs were carried out. These isolates were distributed in 26 sequence types (STs and 49 spa types. The pigmented isolates were scattered in 25 STs, while the non-pigmented isolates were more centralized, which mainly belonged to ST20 (59% and ST25 (13%. Among the 54 non-pigmented isolates, about 20% carried intact crtN and crtM genes. The in vivo assay suggested that comparing with pigmented S. aureus, non-pigmented S. aureus and S. argenteus strains did not show a reduced virulence in murine sepsis models. Therefore, it suggested that there were no significant genetic and virulent differences between pigmented and non-pigmented S. aureus.

Annular and central heavy pigment deposition on the posterior lens capsule in the pigment dispersion syndrome: pigment deposition on the posterior lens capsule in the pigment dispersion syndrome.

Science.gov (United States)

Turgut, Burak; Türkçüoğlu, Peykan; Deniz, Nurettin; Catak, Onur

2008-12-01

To report annular and central heavy pigment deposition on the posterior lens capsule in a case of pigment dispersion syndrome. Case report. A 36-year-old female with bilateral pigment dispersion syndrome presented with progressive decrease in visual acuity in the right eye over the past 1-2 years. Clinical examination revealed the typical findings of pigment dispersion syndrome including bilateral Krunkenberg spindles, iris transillumination defects, and dense trabecular meshwork pigmentation. Remarkably, annular and central dense pigmentation of the posterior lens capsule was noted in the right eye. Annular pigment deposition on the posterior lens capsule may be a rare finding associated with pigment dispersion syndrome. Such a finding suggests that there may be aqueous flow into the retrolental space in some patients with this condition. The way of central pigmentation is the entrance of aqueous to Berger's space. In our case, it is probable that spontaneous detachment of the anterior hyaloid membrane aided this entrance.

New method for detection of gastric cancer by hyperspectral imaging: a pilot study

Science.gov (United States)

Kiyotoki, Shu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao

2013-02-01

We developed a new, easy, and objective method to detect gastric cancer using hyperspectral imaging (HSI) technology combining spectroscopy and imaging A total of 16 gastroduodenal tumors removed by endoscopic resection or surgery from 14 patients at Yamaguchi University Hospital, Japan, were recorded using a hyperspectral camera (HSC) equipped with HSI technology Corrected spectral reflectance was obtained from 10 samples of normal mucosa and 10 samples of tumors for each case The 16 cases were divided into eight training cases (160 training samples) and eight test cases (160 test samples) We established a diagnostic algorithm with training samples and evaluated it with test samples Diagnostic capability of the algorithm for each tumor was validated, and enhancement of tumors by image processing using the HSC was evaluated The diagnostic algorithm used the 726-nm wavelength, with a cutoff point established from training samples The sensitivity, specificity, and accuracy rates of the algorithm's diagnostic capability in the test samples were 78.8% (63/80), 92.5% (74/80), and 85.6% (137/160), respectively Tumors in HSC images of 13 (81.3%) cases were well enhanced by image processing Differences in spectral reflectance between tumors and normal mucosa suggested that tumors can be clearly distinguished from background mucosa with HSI technology.

A survey of landmine detection using hyperspectral imaging

Science.gov (United States)

Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo

2017-02-01

Hyperspectral imaging is a trending technique in remote sensing that finds its application in many different areas, such as agriculture, mapping, target detection, food quality monitoring, etc. This technique gives the ability to remotely identify the composition of each pixel of the image. Therefore, it is a natural candidate for the purpose of landmine detection, thanks to its inherent safety and fast response time. In this paper, we will present the results of several studies that employed hyperspectral imaging for the purpose of landmine detection, discussing the different signal processing techniques used in this framework for hyperspectral image processing and target detection. Our purpose is to highlight the progresses attained in the detection of landmines using hyperspectral imaging and to identify possible perspectives for future work, in order to achieve a better detection in real-time operation mode.

Polarization-Sensitive Hyperspectral Imaging in vivo: A Multimode Dermoscope for Skin Analysis

Science.gov (United States)

Vasefi, Fartash; MacKinnon, Nicholas; Saager, Rolf B.; Durkin, Anthony J.; Chave, Robert; Lindsley, Erik H.; Farkas, Daniel L.

2014-05-01

Attempts to understand the changes in the structure and physiology of human skin abnormalities by non-invasive optical imaging are aided by spectroscopic methods that quantify, at the molecular level, variations in tissue oxygenation and melanin distribution. However, current commercial and research systems to map hemoglobin and melanin do not correlate well with pathology for pigmented lesions or darker skin. We developed a multimode dermoscope that combines polarization and hyperspectral imaging with an efficient analytical model to map the distribution of specific skin bio-molecules. This corrects for the melanin-hemoglobin misestimation common to other systems, without resorting to complex and computationally intensive tissue optical models. For this system's proof of concept, human skin measurements on melanocytic nevus, vitiligo, and venous occlusion conditions were performed in volunteers. The resulting molecular distribution maps matched physiological and anatomical expectations, confirming a technologic approach that can be applied to next generation dermoscopes and having biological plausibility that is likely to appeal to dermatologists.

An interactive tool for semi-automatic feature extraction of hyperspectral data

Science.gov (United States)

Kovács, Zoltán; Szabó, Szilárd

2016-09-01

The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in - Hyperspectral Data Analyst (HypDA) - for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

Molecular evolution of the cone visual pigments in the pure rod-retina of the nocturnal gecko, Gekko gekko.

Science.gov (United States)

Yokoyama, S; Blow, N S

2001-10-03

We have isolated a full-length cDNA encoding a putative ultraviolet (UV)-sensitive visual pigment of the Tokay gecko (Gekko gekko). This clone has 57 and 59% sequence similarities to the gecko RH2 and MWS pigment genes, respectively, but it shows 87% similarity to the UV pigment gene of the American chameleon (Anolis carolinensis). The evolutionary rates of amino acid replacement are significantly higher in the three gecko pigments than in the corresponding chameleon pigments. The accelerated evolutionary rates reflect not only the transition from cones to rods in the retina but also the blue-shift in the absorption spectra of the gecko pigments.

Characterization of Angle Dependent Color Travel of Printed Multi-Color Effect Pigment on Different Color Substrates

Directory of Open Access Journals (Sweden)

Mirica Karlovits

2015-03-01

Full Text Available Color-travel pigments, which exhibit much more extensive color change as well provide angle-dependent optical effect can be used in many industrial products. In present paper the multi-color effect pigment printed on three different foils with different background color (black, silver and transparent was investigated. The pigment was based on synthetically produced transparent silicon dioxide platelets coated with titanium dioxide. CIEL*a*b* values and reflection of prints were measured by multi-angle spectrophotometer at constant illumination at an angle of 45º and different viewing angles (-15º, 15°, 25º, 45º, 75º and 110º were used. The measurements of printed multi-color pigment showed that CIEL*a*b* color coordinates varied to great extents, depending on detection angles as well on color of the printing substrate. The study revealed that pigmnet printed on black background obtained significant change in color. The study has also shown that when viewing angle increases, the reflection curves decreases.

Hyperspectral wide gap second derivative analysis for in vivo detection of cervical intraepithelial neoplasia

Science.gov (United States)

Zheng, Wenli; Wang, Chaojian; Chang, Shufang; Zhang, Shiwu; Xu, Ronald X.

2015-12-01

Hyperspectral reflectance imaging technique has been used for in vivo detection of cervical intraepithelial neoplasia. However, the clinical outcome of this technique is suboptimal owing to multiple limitations such as nonuniform illumination, high-cost and bulky setup, and time-consuming data acquisition and processing. To overcome these limitations, we acquired the hyperspectral data cube in a wavelength ranging from 600 to 800 nm and processed it by a wide gap second derivative analysis method. This method effectively reduced the image artifacts caused by nonuniform illumination and background absorption. Furthermore, with second derivative analysis, only three specific wavelengths (620, 696, and 772 nm) are needed for tissue classification with optimal separability. Clinical feasibility of the proposed image analysis and classification method was tested in a clinical trial where cervical hyperspectral images from three patients were used for classification analysis. Our proposed method successfully classified the cervix tissue into three categories of normal, inflammation and high-grade lesion. These classification results were coincident with those by an experienced gynecology oncologist after applying acetic acid. Our preliminary clinical study has demonstrated the technical feasibility for in vivo and noninvasive detection of cervical neoplasia without acetic acid. Further clinical research is needed in order to establish a large-scale diagnostic database and optimize the tissue classification technique.

[Research on hyperspectral remote sensing in monitoring snow contamination concentration].

Science.gov (United States)

Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan

2011-05-01

Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.

Single crystal X-ray structure of the artists’ pigment zinc yellow

DEFF Research Database (Denmark)

Simonsen, Kim Pilkjær; Christiansen, Marie Bitsch; Vinum, Morten Gotthold

2017-01-01

electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and powder X-ray diffraction (PXRD), showed that the synthesised products and the industrial pigment were identical. Single-crystal X-ray crystallography......The artists’ pigment zinc yellow is in general described as a complex potassium zinc chromate with the empirical formula 4ZnCrO4·K2O·3H2O. Even though the pigment has been in use since the second half of the 19th century also in large-scale industrial applications, the exact structure had hitherto...... been unknown. In this work, zinc yellow was synthesised by precipitation from an aqueous solution of zinc nitrate and potassium chromate under both neutral and basic conditions, and the products were compared with the pigment used in industrial paints. Analyses by Raman microscopy (MRS), scanning...

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

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Chang Li

2016-07-01

Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

Research advances in reflectance spectra of plant leafs

Science.gov (United States)

Zhu, Taotao; Yang, Ting; Guo, Yanxin; Xu, Jingqi; Chang, Wandong; Fang, Siyi; Zhu, Kangkang; Xu, Tingyan

2018-02-01

Leaves are an important factor when we study plants because their water content, pigment content and nutrient content of leaves can reflect the current growth status of the whole plant. The methods of spectral diagnosis technology or image technology mainly are the pre-detection technique which can be used to invert the color, texture and spectral reflectance of the leaves. From this we can obtain the changes of the internal components and the external morphological characteristics of the plant leaves in different states changes. In this paper, the reflection spectral response mechanism of plant water content, pigment and nutrient elements at domestic and overseas are reviewed and compared.

Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data

Science.gov (United States)

Shi, Yue; Huang, Wenjiang; Zhou, Xianfeng

2017-04-01

Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.

Characterizing Intimate Mixtures of Materials in Hyperspectral Imagery with Albedo-based and Kernel-based Approaches

Science.gov (United States)

2015-09-01

Automated classification of built-up areas using neural networks and subpixel demixing methods on multispectral/hyperspectral data,” Proceedings of...scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed...following Hapke 9 (1993); and Mustard and Pieters 18 (1987)) assuming the reflectance spectra are bidirectional . SSA spectra were also generated

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

Energy Technology Data Exchange (ETDEWEB)

Serrato, M.; Jungho, I.; Jensen, J.; Jensen, R.; Gladden, J.; Waugh, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.

Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

Directory of Open Access Journals (Sweden)

Mike Serrato

2012-01-01

Full Text Available This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1 estimate leaf-area-index (LAI of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP, and machine learning regression trees, and (2 map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution, collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R2 > 0.80. The use of REPs failed to accurately predict LAI (R2 < 0.2. The use of the MTMF-derived metrics (matched filter scores and infeasibility and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches ( < 1 m found on the sites.

Vegetation Cover Analysis Of Hazardous Waste Sites In Utah And Arizona Using Hyperspectral Remote Sensing

International Nuclear Information System (INIS)

Serrato, M.; Jungho, I.; Jensen, J.; Jensen, R.; Gladden, J.; Waugh, J.

2012-01-01

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R 2 > 0.80). The use of REPs failed to accurately predict LAI (R 2 < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.

Hyperspectral Remote Sensing of Terrestrial Ecosystem Productivity from ISS

Science.gov (United States)

Huemmrich, K. F.; Campbell, P. K. E.; Gao, B. C.; Flanagan, L. B.; Goulden, M.

2017-12-01

Data from the Hyperspectral Imager for Coastal Ocean (HICO), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. The ISS orbit introduces both limitations and opportunities for observing ecosystem dynamics. Twenty six HICO images were used from four study sites representing different vegetation types: grasslands, shrubland, and forest. Gross ecosystem production (GEP) data from eddy covariance were matched with HICO-derived spectra. Multiple algorithms were successful relating spectral reflectance with GEP, including: Spectral Vegetation Indices (SVI), SVI in a light use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to Multiband Hyperspectral Indices (MHI) from stepwise regressions and Partial Least Squares Regression (PLSR). Algorithms were able to achieve r2 better than 0.7 for both GEP at the overpass time and daily GEP. These algorithms were successful using a diverse set of observations combining data from multiple years, multiple times during growing season, different times of day, with different view angles, and different vegetation types. The demonstrated robustness of the algorithms presented in this study over these conditions provides some confidence in mapping spatial patterns of GEP, describing variability within fields as well as the regional patterns based only on spectral reflectance information. The ISS orbit provides periods with multiple observations collected at different times of the day within a period of a few days. Diurnal GEP patterns were estimated comparing the half-hourly average GEP from the flux tower against HICO estimates of GEP (r2=0.87) if morning, midday, and afternoon observations were available for average fluxes in the time period.

Iris phenotypes and pigment dispersion caused by genes influencing pigmentation.

Science.gov (United States)

Anderson, Michael G; Hawes, Norman L; Trantow, Colleen M; Chang, Bo; John, Simon W M

2008-10-01

Spontaneous mutations altering mouse coat colors have been a classic resource for discovery of numerous molecular pathways. Although often overlooked, the mouse iris is also densely pigmented and easily observed, thus representing a similarly powerful opportunity for studying pigment cell biology. Here, we present an analysis of iris phenotypes among 16 mouse strains with mutations influencing melanosomes. Many of these strains exhibit biologically and medically relevant phenotypes, including pigment dispersion, a common feature of several human ocular diseases. Pigment dispersion was identified in several strains with mutant alleles known to influence melanosomes, including beige, light, and vitiligo. Pigment dispersion was also detected in the recently arising spontaneous coat color variant, nm2798. We have identified the nm2798 mutation as a missense mutation in the Dct gene, an identical re-occurrence of the slaty light mutation. These results suggest that dysregulated events of melanosomes can be potent contributors to the pigment dispersion phenotype. Combined, these findings illustrate the utility of studying iris phenotypes as a means of discovering new pathways, and re-linking old ones, to processes of pigmented cells in health and disease.

Skin Pigmentation Disorders

Science.gov (United States)

Pigmentation means coloring. Skin pigmentation disorders affect the color of your skin. Your skin gets its color from a pigment called melanin. Special cells in the skin make melanin. When these cells become damaged or ...

Real-time recursive hyperspectral sample and band processing algorithm architecture and implementation

CERN Document Server

Chang, Chein-I

2017-01-01

This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data.

Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery

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Poonsak Miphokasap

2018-04-01

Full Text Available This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR and Support Vector Regression (SVR were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management.

Dried fruits quality assessment by hyperspectral imaging

Science.gov (United States)

Serranti, Silvia; Gargiulo, Aldo; Bonifazi, Giuseppe

2012-05-01

Dried fruits products present different market values according to their quality. Such a quality is usually quantified in terms of freshness of the products, as well as presence of contaminants (pieces of shell, husk, and small stones), defects, mould and decays. The combination of these parameters, in terms of relative presence, represent a fundamental set of attributes conditioning dried fruits humans-senses-detectable-attributes (visual appearance, organolectic properties, etc.) and their overall quality in terms of marketable products. Sorting-selection strategies exist but sometimes they fail when a higher degree of detection is required especially if addressed to discriminate between dried fruits of relatively small dimensions and when aiming to perform an "early detection" of pathogen agents responsible of future moulds and decays development. Surface characteristics of dried fruits can be investigated by hyperspectral imaging (HSI). In this paper, specific and "ad hoc" applications addressed to propose quality detection logics, adopting a hyperspectral imaging (HSI) based approach, are described, compared and critically evaluated. Reflectance spectra of selected dried fruits (hazelnuts) of different quality and characterized by the presence of different contaminants and defects have been acquired by a laboratory device equipped with two HSI systems working in two different spectral ranges: visible-near infrared field (400-1000 nm) and near infrared field (1000-1700 nm). The spectra have been processed and results evaluated adopting both a simple and fast wavelength band ratio approach and a more sophisticated classification logic based on principal component (PCA) analysis.

UAV-Based Hyperspectral Remote Sensing for Precision Agriculture: Challenges and Opportunities

Science.gov (United States)

Angel, Y.; Parkes, S. D.; Turner, D.; Houborg, R.; Lucieer, A.; McCabe, M.

2017-12-01

Modern agricultural production relies on monitoring crop status by observing and measuring variables such as soil condition, plant health, fertilizer and pesticide effect, irrigation and crop yield. Managing all of these factors is a considerable challenge for crop producers. As such, providing integrated technological solutions that enable improved diagnostics of field condition to maximize profits, while minimizing environmental impacts, would be of much interest. Such challenges can be addressed by implementing remote sensing systems such as hyperspectral imaging to produce precise biophysical indicator maps across the various cycles of crop development. Recent progress in unmanned aerial vehicles (UAVs) have advanced traditional satellite-based capabilities, providing a capacity for high-spatial, spectral and temporal response. However, while some hyperspectral sensors have been developed for use onboard UAVs, significant investment is required to develop a system and data processing workflow that retrieves accurately georeferenced mosaics. Here we explore the use of a pushbroom hyperspectral camera that is integrated on-board a multi-rotor UAV system to measure the surface reflectance in 272 distinct spectral bands across a wavelengths range spanning 400-1000 nm, and outline the requirement for sensor calibration, integration onto a stable UAV platform enabling accurate positional data, flight planning, and development of data post-processing workflows for georeferenced mosaics. The provision of high-quality and geo-corrected imagery facilitates the development of metrics of vegetation health that can be used to identify potential problems such as production inefficiencies, diseases and nutrient deficiencies and other data-streams to enable improved crop management. Immense opportunities remain to be exploited in the implementation of UAV-based hyperspectral sensing (and its combination with other imaging systems) to provide a transferable and scalable

Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

Science.gov (United States)

Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha

2011-01-01

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. PMID:22163894

Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data

Directory of Open Access Journals (Sweden)

Gregory P. Asner

2012-11-01

Full Text Available Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.

Recent Advances in Techniques for Hyperspectral Image Processing

Science.gov (United States)

Plaza, Antonio; Benediktsson, Jon Atli; Boardman, Joseph W.; Brazile, Jason; Bruzzone, Lorenzo; Camps-Valls, Gustavo; Chanussot, Jocelyn; Fauvel, Mathieu; Gamba, Paolo; Gualtieri, Anthony;

In vivo pump-probe microscopy of melanoma and pigmented lesions

Science.gov (United States)

Wilson, Jesse W.; Degan, Simone; Mitropoulos, Tanya; Selim, M. Angelica; Zhang, Jennifer Y.; Warren, Warren S.

2012-03-01

A growing number of dermatologists and pathologists are concerned that the rapidly rising incidence of melanoma reflects not a true 'epidemic' but an increasing tendency to overdiagnose pigmented lesions. Addressing this problem requires both a better understanding of early-stage melanoma and new diagnostic criteria based on more than just cellular morphology and architecture. Here we present a method for in-vivo optical microscopy that utilizes pump-probe spectroscopy to image the distribution of the two forms of melanin in skin: eumelanin and pheomelanin. Images are acquired in a scanning microscope with a sensitive modulation transfer technique by analyzing back-scattered probe light with a lock-in amplifier. Early-stage melanoma is studied in a human skin xenografted mouse model. Individual melanocytes have been observed, in addition to pigmented keratinocytes. Combining the pump-probe images simultaneously with other noninvasive laser microscopy methods (confocal reflectance, multiphoton autofluorescence, and second harmonic generation) allows visualization of the skin architecture, framing the functional pump-probe image in the context of the surrounding tissue morphology. It is found that pump-probe images of melanin can be acquired with low peak intensities, enabling wide field-of-view pigmentation surveys. Finally, we investigate the diagnostic potential of the additional chemical information available from pump-probe microscopy.

Ultraviolet reflectance by the cere of raptors

Science.gov (United States)

Mougeot, François; Arroyo, Beatriz E

2006-01-01

Ultraviolet (UV) signals have been shown to play key roles in social and sexual signalling in birds. Using a spectrophotometer, we analysed the colour of the cere (skin above the beak) of a diurnal raptor, the Montagu's harrier (Circus pygargus), and show that it reflects in the UV part of the spectrum. The cere is a well-known sexual signal in raptors, with carotenoid based pigmentation being indicative of quality. We thus hypothesized that UV reflectance also signals quality. Accordingly, we found that in our sample of wild males, the location of the UV peak was related to the orangeness of cere and correlated with male body mass and condition (mass corrected for size). Also, males with brighter UV were mated to females that laid earlier, as expected if UV reflectance relates to a male's quality and attractiveness. Future studies should investigate the relationships between UV reflectance and carotenoid pigmentation of cere, and test how UV reflectance influences mate choice. PMID:17148356

Novel hyperspectral prediction method and apparatus

Science.gov (United States)

Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

2009-05-01

Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

Fusion of LBP and SWLD using spatio-spectral information for hyperspectral face recognition

Science.gov (United States)

Xie, Zhihua; Jiang, Peng; Zhang, Shuai; Xiong, Jinquan

2018-01-01

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Hyperspectral Cubesat Constellation for Rapid Natural Hazard Response

Science.gov (United States)

Mandl, D.; Huemmrich, K. F.; Ly, V. T.; Handy, M.; Ong, L.; Crum, G.

2015-12-01

With the advent of high performance space networks that provide total coverage for Cubesats, the paradigm for low cost, high temporal coverage with hyperspectral instruments becomes more feasible. The combination of ground cloud computing resources, high performance with low power consumption onboard processing, total coverage for the cubesats and social media provide an opprotunity for an architecture that provides cost-effective hyperspectral data products for natural hazard response and decision support. This paper provides a series of pathfinder efforts to create a scalable Intelligent Payload Module(IPM) that has flown on a variety of airborne vehicles including Cessna airplanes, Citation jets and a helicopter and will fly on an Unmanned Aerial System (UAS) hexacopter to monitor natural phenomena. The IPM's developed thus far were developed on platforms that emulate a satellite environment which use real satellite flight software, real ground software. In addition, science processing software has been developed that perform hyperspectral processing onboard using various parallel processing techniques to enable creation of onboard hyperspectral data products while consuming low power. A cubesat design was developed that is low cost and that is scalable to larger consteallations and thus can provide daily hyperspectral observations for any spot on earth. The design was based on the existing IPM prototypes and metrics that were developed over the past few years and a shrunken IPM that can perform up to 800 Mbps throughput. Thus this constellation of hyperspectral cubesats could be constantly monitoring spectra with spectral angle mappers after Level 0, Level 1 Radiometric Correction, Atmospheric Correction processing. This provides the opportunity daily monitoring of any spot on earth on a daily basis at 30 meter resolution which is not available today.

Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing.

Science.gov (United States)

Hakkenberg, C R; Peet, R K; Urban, D L; Song, C

2018-01-01

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling. © 2017 by the Ecological Society of America.

Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

Science.gov (United States)

Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

2014-10-01

Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Directory of Open Access Journals (Sweden)

Julie Transon

2018-01-01

Full Text Available In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors’ publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i brings face to face past and future hyperspectral sensors’ applications with Sentinel-2’s and (ii analyzes the applications’ requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

Hyperspectral image classification based on local binary patterns and PCANet

Science.gov (United States)

Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang

2018-04-01

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Dense pigmentation of the posterior lens capsule associated with the pigment dispersion syndrome.

Science.gov (United States)

Lin, Danny Y; Volpicelli, Mark; Singh, Kuldev

2003-12-01

To report an unusual case of pigment dispersion syndrome associated with unilateral dense pigmentation of the posterior lens capsule. Case report. A 59-year-old male with bilateral pigment dispersion syndrome presented with progressive decrease in visual acuity in the left eye over the past 10 to 20 years. Clinical examination revealed the typical findings of pigment dispersion syndrome including the presence of bilateral Krunkenberg spindles, iris transillumination defects, and heavy trabecular meshwork pigmentation. Of note, there was remarkably dense pigmentation of the posterior lens capsule in the eye with decreased visual acuity. Pigmentation of the posterior lens capsule may be a rare finding associated with pigment dispersion syndrome. Such a finding suggests that there may be aqueous flow into the retrolental space in some patients with this condition. The optimal treatment of this unusual condition remains undetermined.

Optical Algorithms at Satellite Wavelengths for Total Suspended Matter in Tropical Coastal Waters

OpenAIRE

Ouillon, Sylvain; Douillet, Pascal; Petrenko, Anne; Neveux, Jacques; Dupouy, C?cile; Froidefond, Jean-Marie; Andr?fou?t, Serge; Mu?oz-Caravaca, Alain

2008-01-01

Is it possible to derive accurately Total Suspended Matter concentration or its proxy, turbidity, from remote sensing data in tropical coastal lagoon waters? To investigate this question, hyperspectral remote sensing reflectance, turbidity and chlorophyll pigment concentration were measured in three coral reef lagoons. The three sites enabled us to get data over very diverse environments: oligotrophic and sediment-poor waters in the southwest lagoon of New Caledonia, eutrophic waters in the C...

Deriving seasonal dynamics in ecosystem properties of semi-arid savanna grasslands from in situ-based hyperspectral reflectance

DEFF Research Database (Denmark)

Tagesson, Håkan Torbern; Fensholt, Rasmus; Huber, S.

2015-01-01

strongly affected by solar zenith angles and sensor viewing geometry, as were many combinations of visible wavelengths. This study provides analyses based upon novel multi-angular hyperspectral data for validation of Earth-observation-based properties of semi-arid ecosystems, as well as insights...... between normalised difference spectral indices (NDSIs) and the measured ecosystem properties. Finally, the effects of variable sun sensor viewing geometry on different NDSI wavelength combinations were analysed. The wavelengths with the strongest correlation to seasonal dynamics in ecosystem properties...

Recent progress of push-broom infrared hyper-spectral imager in SITP

Science.gov (United States)

Wang, Yueming; Hu, Weida; Shu, Rong; Li, Chunlai; Yuan, Liyin; Wang, Jianyu

2017-02-01

In the past decades, hyper-spectral imaging technologies were well developed in SITP, CAS. Many innovations for system design and key parts of hyper-spectral imager were finished. First airborne hyper-spectral imager operating from VNIR to TIR in the world was emerged in SITP. It is well known as OMIS(Operational Modular Imaging Spectrometer). Some new technologies were introduced to improve the performance of hyper-spectral imaging system in these years. A high spatial space-borne hyper-spectral imager aboard Tiangong-1 spacecraft was launched on Sep.29, 2011. Thanks for ground motion compensation and high optical efficiency prismatic spectrometer, a large amount of hyper-spectral imagery with high sensitivity and good quality were acquired in the past years. Some important phenomena were observed. To diminish spectral distortion and expand field of view, new type of prismatic imaging spectrometer based curved prism were proposed by SITP. A prototype of hyper-spectral imager based spherical fused silica prism were manufactured, which can operate from 400nm 2500nm. We also made progress in the development of LWIR hyper-spectral imaging technology. Compact and low F number LWIR imaging spectrometer was designed, manufactured and integrated. The spectrometer operated in a cryogenically-cooled vacuum box for background radiation restraint. The system performed well during flight experiment in an airborne platform. Thanks high sensitivity FPA and high performance optics, spatial resolution and spectral resolution and SNR of system are improved enormously. However, more work should be done for high radiometric accuracy in the future.

Congenital Simple Hamartoma of Retinal Pigment Epithelium: Clinical and Imaging Findings

Directory of Open Access Journals (Sweden)

Mehmet Yasin Teke

2012-01-01

Full Text Available Congenital simple hamartoma of retinal pigment epithelium (CSHRPE is a rare, asymptomatic, and incidentally detected benign lesion. However, it is very important to do the differential diagnosis from other pigmented retinal lesions. Its clinical presentation and imaging findings are very helpful in doing this differentiation. This paper presents clinical and imaging findings of a 56-year-old woman with incidentally detected CSHRPE. The lesion was small, heavily pigmented, well circumscribed, and slightly elevated. Optical coherence tomography (OCT scanning was diagnostic and showed an elevated retina at the site of the lesion, increased optical reflectivity on its inner surface, optical shadowing of deeper structures, and clearly cut tumor margins. Ocular ultrasonography, fluorescein angiography, and fundus autofluorescence imaging which is firstly described in this report did not show any characteristic finding.

A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

Science.gov (United States)

Hu, Bin-Lin; Hao, Shi-Jing; Sun, De-Xin; Liu, Yin-Nian

2017-09-01

A novel scene-based non-uniformity correction (NUC) method for short-wavelength infrared (SWIR) push-broom hyperspectral sensors is proposed and evaluated. This method relies on the assumption that for each band there will be ground objects with similar reflectance to form uniform regions when a sufficient number of scanning lines are acquired. The uniform regions are extracted automatically through a sorting algorithm, and are used to compute the corresponding NUC coefficients. SWIR hyperspectral data from airborne experiment are used to verify and evaluate the proposed method, and results show that stripes in the scenes have been well corrected without any significant information loss, and the non-uniformity is less than 0.5%. In addition, the proposed method is compared to two other regular methods, and they are evaluated based on their adaptability to the various scenes, non-uniformity, roughness and spectral fidelity. It turns out that the proposed method shows strong adaptability, high accuracy and efficiency.

High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging

Directory of Open Access Journals (Sweden)

Piyush Pandey

2017-08-01

Full Text Available Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo. These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N, phosphorus (P, potassium (K, magnesium (Mg, calcium (Ca, and sulfur (S, and micronutrients sodium (Na, iron (Fe, manganese (Mn, boron (B, copper (Cu, and zinc (Zn. Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [R2 = 0.93 and RPD (Ratio of Performance to Deviation = 3.8]. All macronutrients were also quantified satisfactorily (R2 from 0.69 to 0.92, RPD from 1.62 to 3.62, with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy (R2 from 0.19 to 0.86, RPD from 1.09 to 2.69 than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily (R2 < 0.3 and RPD < 1.2. This study suggested

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging.

Directory of Open Access Journals (Sweden)

Ye Sun

Full Text Available This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS. A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2 of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE and root mean square error (RMSE were in a range of 2.03-53.40×10(-4 and 0.011-0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA, with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.

Medical hyperspectral imaging: a review

Science.gov (United States)

Lu, Guolan; Fei, Baowei

2014-01-01

Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

Manifold regularization for sparse unmixing of hyperspectral images.

Science.gov (United States)

Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

2016-01-01

Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

Science.gov (United States)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

Dental caries imaging using hyperspectral stimulated Raman scattering microscopy

Science.gov (United States)

Wang, Zi; Zheng, Wei; Jian, Lin; Huang, Zhiwei

2016-03-01

We report the development of a polarization-resolved hyperspectral stimulated Raman scattering (SRS) imaging technique based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of dental caries. In our imaging system, hyperspectral SRS images (512×512 pixels) in both fingerprint region (800-1800 cm-1) and high-wavenumber region (2800-3600 cm-1) are acquired in minutes by scanning the wavelength of OPO output, which is a thousand times faster than conventional confocal micro Raman imaging. SRS spectra variations from normal enamel to caries obtained from the hyperspectral SRS images show the loss of phosphate and carbonate in the carious region. While polarization-resolved SRS images at 959 cm-1 demonstrate that the caries has higher depolarization ratio. Our results demonstrate that the polarization resolved-hyperspectral SRS imaging technique developed allows for rapid identification of the biochemical and structural changes of dental caries.

Airborne hyperspectral remote sensing in Italy

Science.gov (United States)

Bianchi, Remo; Marino, Carlo M.; Pignatti, Stefano

1994-12-01

The Italian National Research Council (CNR) in the framework of its `Strategic Project for Climate and Environment in Southern Italy' established a new laboratory for airborne hyperspectral imaging devoted to environmental problems. Since the end of June 1994, the LARA (Laboratorio Aereo per Ricerche Ambientali -- Airborne Laboratory for Environmental Studies) Project is fully operative to provide hyperspectral data to the national and international scientific community by means of deployments of its CASA-212 aircraft carrying the Daedalus AA5000 MIVIS (multispectral infrared and visible imaging spectrometer) system. MIVIS is a modular instrument consisting of 102 spectral channels that use independent optical sensors simultaneously sampled and recorded onto a compact computer compatible magnetic tape medium with a data capacity of 10.2 Gbytes. To support the preprocessing and production pipeline of the large hyperspectral data sets CNR housed in Pomezia, a town close to Rome, a ground based computer system with a software designed to handle MIVIS data. The software (MIDAS-Multispectral Interactive Data Analysis System), besides the data production management, gives to users a powerful and highly extensible hyperspectral analysis system. The Pomezia's ground station is designed to maintain and check the MIVIS instrument performance through the evaluation of data quality (like spectral accuracy, signal to noise performance, signal variations, etc.), and to produce, archive, and diffuse MIVIS data in the form of geometrically and radiometrically corrected data sets on low cost and easy access CC media.

Hyperspectral signature analysis of skin parameters

Science.gov (United States)

Vyas, Saurabh; Banerjee, Amit; Garza, Luis; Kang, Sewon; Burlina, Philippe

2013-02-01

The temporal analysis of changes in biological skin parameters, including melanosome concentration, collagen concentration and blood oxygenation, may serve as a valuable tool in diagnosing the progression of malignant skin cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, and point-of-care diagnostics, amongst others. We propose an approach to estimate in vivo skin parameters using a forward computational model based on Kubelka-Munk theory and the Fresnel Equations. We use this model to map the skin parameters to their corresponding hyperspectral signature. We then use machine learning based regression to develop an inverse map from hyperspectral signatures to skin parameters. In particular, we employ support vector machine based regression to estimate the in vivo skin parameters given their corresponding hyperspectral signature. We build on our work from SPIE 2012, and validate our methodology on an in vivo dataset. This dataset consists of 241 signatures collected from in vivo hyperspectral imaging of patients of both genders and Caucasian, Asian and African American ethnicities. In addition, we also extend our methodology past the visible region and through the short-wave infrared region of the electromagnetic spectrum. We find promising results when comparing the estimated skin parameters to the ground truth, demonstrating good agreement with well-established physiological precepts. This methodology can have potential use in non-invasive skin anomaly detection and for developing minimally invasive pre-screening tools.

An investigation of multispectral imaging for the mapping of pigments in paintings

Science.gov (United States)

Zhao, Yonghui; Berns, Roy S.; Taplin, Lawrence A.; Coddington, James

2008-02-01

Compared with colorimetric imaging, multispectral imaging has the advantage of retrieving spectral reflectance factor of each pixel of a painting. Using this spectral information, pigment mapping is concerned with decomposing the spectrum into its constituent pigments and their relative contributions. The output of pigment mapping is a series of spatial concentration maps of the pigments comprising the painting. This approach was used to study Vincent van Gogh's The Starry Night. The artist's palette was approximated using ten oil pigments, selected from a large database of pigments used in oil paintings and a priori analytical research on one of his self portraits, executed during the same time period. The pigment mapping was based on single-constant Kubelka-Munk theory. It was found that the region of blue sky where the stars were located contained, predominantly, ultramarine blue while the swirling sky and region surrounding the moon contained, predominantly, cobalt blue. Emerald green, used in light bluish-green brushstrokes surrounding the moon, was not used to create the dark green in the cypresses. A measurement of lead white from Georges Seurat's La Grande Jatte was used as the white when mapping The Starry Night. The absorption and scattering properties of this white were replaced with a modern dispersion of lead white in linseed oil and used to simulate the painting's appearance before the natural darkening and yellowing of lead white oil paint. Pigment mapping based on spectral imaging was found to be a viable and practical approach for analyzing pigment composition, providing new insight into an artist's working method, the possibility for aiding in restorative inpainting, and lighting design.

Hyperspectral stimulated emission depletion microscopy and methods of use thereof

Science.gov (United States)

Timlin, Jerilyn A; Aaron, Jesse S

2014-04-01

A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

Spatial-temporal distribution of phytoplankton pigments in relation to nutrient status in Jiaozhou Bay, China

Science.gov (United States)

Yao, Peng; Yu, Zhigang; Deng, Chunmei; Liu, Shuxia; Zhen, Yu

2010-10-01

We conducted studies of phytoplankton and hydrological variables in a semi-enclosed bay in northern China to understand the spatial-temporal variability and relationship between these variables. Samples were collected during seven cruises in Jiaozhou Bay from November 2003 to October 2004, and were analyzed for temperature, nutrients and phytoplankton pigments. Pigments from eight possible phytoplankton classes (Diatoms, Dinoflagellates, Chlorophyceae, Prasinophyceae, Chrysophyceae, Haptophyceae, Cryptophyceae and Caynophyceae) were detected in surface water by high performance liquid chromatography (HPLC). Phytoplankton pigment and nutrient concentrations in Jiaozhou Bay were spatially and temporally variable, and most of them were highest in the northern and eastern parts of the sampling regions in spring (May) and summer (August), close to areas of shellfish culturing, river estuaries, dense population and high industrialization, reflecting human activities. Chlorophyll a was recorded in all samples, with an annual mean concentration of 1.892 μg L -1, and fucoxanthin was the most abundant accessory pigment, with a mean concentration of 0.791 μg L -1. The highest concentrations of chlorophyll a (15.299 μg L -1) and fucoxanthin (9.417 μg L -1) were observed in May 2004 at the station close to the Qingdao Xiaogang Ferry, indicating a spring bloom of Diatoms in this area. Although chlorophyll a and other biomarker pigments showed significant correlations, none of them showed strong correlations with temperature and nutrients, suggesting an apparent de-coupling between the pigments and these hydrological variables. The nutrient composition and phytoplankton community composition of Jiaozhou Bay have changed significantly in the past several decades, reflecting the increasing nutrient concentrations and decline of phytoplankton cell abundance. The unchanged total chlorophyll a levels indicated that smaller species have filled the niche vacated by the larger

Looking for Common Fingerprints in Leonardo's Pupils Using Nondestructive Pigment Characterization.

Science.gov (United States)

Bonizzoni, Letizia; Gargano, Marco; Ludwig, Nicola; Martini, Marco; Galli, Anna

2017-08-01

Non-invasive, portable analytical techniques are becoming increasingly widespread for the study and conservation in the field of cultural heritage, proving that a good data handling, supported by a deep knowledge of the techniques themselves, and the right synergy can give surprisingly substantial results when using portable but reliable instrumentation. In this work, pigment characterization was carried out on 21 Leonardesque paintings applying in situ X-ray fluorescence (XRF) and fiber optic reflection spectroscopy (FORS) analyses. In-depth data evaluation allowed to get information on the color palette and the painting technique of the different artists and workshops . Particular attention was paid to green pigments (for which a deeper study of possible pigments and alterations was performed with FORS analyses), flesh tones (for which a comparison with available data from cross-sections was made), and ground preparation.

Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM+, OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area

Science.gov (United States)

Hoang, Nguyen Tien; Koike, Katsuaki

2018-03-01

Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images (> 95% and > 42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces.

Manifold learning based feature extraction for classification of hyperspectral data

CSIR Research Space (South Africa)

Lunga, D

2014-01-01

Full Text Available in analysis of hyperspectral imagery. High spectral resolution and the typically continuous bands of hyperspectral image (HSI) data enable discrimination between spectrally similar targets of interest, provide capability to estimate within pixel abundances...

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

Science.gov (United States)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

Remote sensing of key grassland nutrients using hyperspectral techniques in KwaZulu-Natal, South Africa

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Singh, Leeth; Mutanga, Onisimo; Mafongoya, Paramu; Peerbhay, Kabir

2017-07-01

The concentration of forage fiber content is critical in explaining the palatability of forage quality for livestock grazers in tropical grasslands. Traditional methods of determining forage fiber content are usually time consuming, costly, and require specialized laboratory analysis. With the potential of remote sensing technologies, determination of key fiber attributes can be made more accurately. This study aims to determine the effectiveness of known absorption wavelengths for detecting forage fiber biochemicals, neutral detergent fiber, acid detergent fiber, and lignin using hyperspectral data. Hyperspectral reflectance spectral measurements (350 to 2500 nm) of grass were collected and implemented within the random forest (RF) ensemble. Results show successful correlations between the known absorption features and the biochemicals with coefficients of determination (R2) ranging from 0.57 to 0.81 and root mean square errors ranging from 6.97 to 3.03 g/kg. In comparison, using the entire dataset, the study identified additional wavelengths for detecting fiber biochemicals, which contributes to the accurate determination of forage quality in a grassland environment. Overall, the results showed that hyperspectral remote sensing in conjunction with the competent RF ensemble could discriminate each key biochemical evaluated. This study shows the potential to upscale the methodology to a space-borne multispectral platform with similar spectral configurations for an accurate and cost effective mapping analysis of forage quality.

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

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Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

Biologically-inspired data decorrelation for hyper-spectral imaging

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Ghita Ovidiu

2011-01-01

Full Text Available Abstract Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA, linear discriminant analysis (LDA, wavelet decomposition (WD, or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

Hyperspectral Imaging of Forest Resources: The Malaysian Experience

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Mohd Hasmadi, I.; Kamaruzaman, J.

2008-08-01

Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.

Excitation-scanning hyperspectral imaging system for microscopic and endoscopic applications

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Mayes, Sam A.; Leavesley, Silas J.; Rich, Thomas C.

2016-04-01

Current microscopic and endoscopic technologies for cancer screening utilize white-light illumination sources. Hyper-spectral imaging has been shown to improve sensitivity while retaining specificity when compared to white-light imaging in both microscopy and in vivo imaging. However, hyperspectral imaging methods have historically suffered from slow acquisition times due to the narrow bandwidth of spectral filters. Often minutes are required to gather a full image stack. We have developed a novel approach called excitation-scanning hyperspectral imaging that provides 2-3 orders of magnitude increased signal strength. This reduces acquisition times significantly, allowing for live video acquisition. Here, we describe a preliminary prototype excitation-scanning hyperspectral imaging system that can be coupled with endoscopes or microscopes for hyperspectral imaging of tissues and cells. Our system is comprised of three subsystems: illumination, transmission, and imaging. The illumination subsystem employs light-emitting diode arrays to illuminate at different wavelengths. The transmission subsystem utilizes a unique geometry of optics and a liquid light guide. Software controls allow us to interface with and control the subsystems and components. Digital and analog signals are used to coordinate wavelength intensity, cycling and camera triggering. Testing of the system shows it can cycle 16 wavelengths at as fast as 1 ms per cycle. Additionally, more than 18% of the light transmits through the system. Our setup should allow for hyperspectral imaging of tissue and cells in real time.

Nitrogen concentration estimation with hyperspectral LiDAR

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O. Nevalainen

2013-10-01

Full Text Available Agricultural lands have strong impact on global carbon dynamics and nitrogen availability. Monitoring changes in agricultural lands require more efficient and accurate methods. The first prototype of a full waveform hyperspectral Light Detection and Ranging (LiDAR instrument has been developed at the Finnish Geodetic Institute (FGI. The instrument efficiently combines the benefits of passive and active remote sensing sensors. It is able to produce 3D point clouds with spectral information included for every point which offers great potential in the field of remote sensing of environment. This study investigates the performance of the hyperspectral LiDAR instrument in nitrogen estimation. The investigation was conducted by finding vegetation indices sensitive to nitrogen concentration using hyperspectral LiDAR data and validating their performance in nitrogen estimation. The nitrogen estimation was performed by calculating 28 published vegetation indices to ten oat samples grown in different fertilization conditions. Reference data was acquired by laboratory nitrogen concentration analysis. The performance of the indices in nitrogen estimation was determined by linear regression and leave-one-out cross-validation. The results indicate that the hyperspectral LiDAR instrument holds a good capability to estimate plant biochemical parameters such as nitrogen concentration. The instrument holds much potential in various environmental applications and provides a significant improvement to the remote sensing of environment.

Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics.

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Goddijn-Murphy, Lonneke; Peters, Steef; van Sebille, Erik; James, Neil A; Gibb, Stuart

2018-01-01

There is growing global concern over the chemical, biological and ecological impact of plastics in the ocean. Remote sensing has the potential to provide long-term, global monitoring but for marine plastics it is still in its early stages. Some progress has been made in hyperspectral remote sensing of marine macroplastics in the visible (VIS) to short wave infrared (SWIR) spectrum. We present a reflectance model of sunlight interacting with a sea surface littered with macro plastics, based on geometrical optics and the spectral signatures of plastic and seawater. This is a first step towards the development of a remote sensing algorithm for marine plastic using light reflectance measurements in air. Our model takes the colour, transparency, reflectivity and shape of plastic litter into account. This concept model can aid the design of laboratory, field and Earth observation measurements in the VIS-SWIR spectrum and explain the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Non-destructive detection research for hollow heart of potato based on semi-transmission hyperspectral imaging and SVM].

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Huang, Tao; Li, Xiao-yu; Xu, Meng-ling; Jin, Rui; Ku, Jing; Xu, Sen-miao; Wu, Zhen-zhong

2015-01-01

The quality of potato is directly related to their edible value and industrial value. Hollow heart of potato, as a physiological disease occurred inside the tuber, is difficult to be detected. This paper put forward a non-destructive detection method by using semi-transmission hyperspectral imaging with support vector machine (SVM) to detect hollow heart of potato. Compared to reflection and transmission hyperspectral image, semi-transmission hyperspectral image can get clearer image which contains the internal quality information of agricultural products. In this study, 224 potato samples (149 normal samples and 75 hollow samples) were selected as the research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images (390-1 040 nn) of the potato samples, and then the average spectrum of region of interest were extracted for spectral characteristics analysis. Normalize was used to preprocess the original spectrum, and prediction model were developed based on SVM using all wave bands, the accurate recognition rate of test set is only 87. 5%. In order to simplify the model competitive.adaptive reweighed sampling algorithm (CARS) and successive projection algorithm (SPA) were utilized to select important variables from the all 520 spectral variables and 8 variables were selected (454, 601, 639, 664, 748, 827, 874 and 936 nm). 94. 64% of the accurate recognition rate of test set was obtained by using the 8 variables to develop SVM model. Parameter optimization algorithms, including artificial fish swarm algorithm (AFSA), genetic algorithm (GA) and grid search algorithm, were used to optimize the SVM model parameters: penalty parameter c and kernel parameter g. After comparative analysis, AFSA, a new bionic optimization algorithm based on the foraging behavior of fish swarm, was proved to get the optimal model parameter (c=10. 659 1, g=0. 349 7), and the recognition accuracy of 10% were obtained for the AFSA

Ecological-friendly pigments from fungi.

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Durán, Nelson; Teixeira, Maria F S; De Conti, Roseli; Esposito, Elisa

2002-01-01

The dyestuff industry is suffering from the increases in costs of feedstock and energy for dye synthesis, and they are under increasing pressure to minimize the damage to the environment. The industries are continuously looking for cheaper, more environmentally friendly routes to existing dyes. The aim of this minireview is to discuss the most important advances in the fungal pigment area and its interest in biotechnological applications. Characteristic pigments are produced by a wide variety of fungi and the chemical composition of natural dyes are described. These pigments exhibit several biological activities besides cytotoxicity. The synthetic pigments authorized by the EC and in USA and the natural pigments available in the world market are discussed. The obstacle to the exploitation of new natural pigments sources is the food legislation, requesting costly toxicological research, manufacturing costs, and acceptance by consumers. The dislike for novel ingredients is likely to be the biggest impediment for expansion of the pigment list in the near future. If the necessary toxicological testing and the comparison with accepted pigments are made, the fungal pigments, could be acceptable by the current consumer. The potentiality of pigment production in Brazil is possible due to tremendous Amazonian region biodiversity.

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

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Zhang, Tingting; Wei, Wensong; Zhao, Bin; Wang, Ranran; Li, Mingliu; Yang, Liming; Wang, Jianhua; Sun, Qun

2018-03-08

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner.

Postfire soil burn severity mapping with hyperspectral image unmixing

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Peter R. Robichaud; Sarah A. Lewis; Denise Y. M. Laes; Andrew T. Hudak; Raymond F. Kokaly; Joseph A. Zamudio

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after...

Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

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Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

2018-03-01

In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.

High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging

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Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C.

2017-01-01

Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo. These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [R2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily (R2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy (R2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily (R2 designing experiments to vary plant nutrients

[Exploring novel hyperspectral band and key index for leaf nitrogen accumulation in wheat].

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Yao, Xia; Zhu, Yan; Feng, Wei; Tian, Yong-Chao; Cao, Wei-Xing

2009-08-01

The objectives of the present study were to explore new sensitive spectral bands and ratio spectral indices based on precise analysis of ground-based hyperspectral information, and then develop regression model for estimating leaf N accumulation per unit soil area (LNA) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA tinder the various treatments. By adopting the method of reduced precise sampling, the detailed ratio spectral indices (RSI) within the range of 350-2 500 nm were constructed, and the quantitative relationships between LNA (gN m(-2)) and RSI (i, j) were analyzed. It was found that several key spectral bands and spectral indices were suitable for estimating LNA in wheat, and the spectral parameter RSI (990, 720) was the most reliable indicator for LNA in wheat. The regression model based on the best RSI was formulated as y = 5.095x - 6.040, with R2 of 0.814. From testing of the derived equations with independent experiment data, the model on RSI (990, 720) had R2 of 0.847 and RRMSE of 24.7%. Thus, it is concluded that the present hyperspectral parameter of RSI (990, 720) and derived regression model can be reliably used for estimating LNA in winter wheat. These results provide the feasible key bands and technical basis for developing the portable instrument of monitoring wheat nitrogen status and for extracting useful spectral information from remote sensing images.

Hyperspectral microscopy to identify foodborne bacteria with optimum lighting source

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Hyperspectral microscopy is an emerging technology for rapid detection of foodborne pathogenic bacteria. Since scattering spectral signatures from hyperspectral microscopic images (HMI) vary with lighting sources, it is important to select optimal lights. The objective of this study is to compare t...

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

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Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

Establishment of the Relationship between the Photochemical Reflectance Index and Canopy Light Use Efficiency Using Multi-angle Hyperspectral Observations

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Zhang, Qian; Chen, Jing; Zhang, Yongguang; Qiu, Feng; Fan, Weiliang; Ju, Weimin

2017-04-01

The gross primary production (GPP) of terrestrial ecosystems constitutes the largest global land carbon flux and exhibits significant spatial and temporal variations. Due to its wide spatial coverage, remote sensing technology is shown to be useful for improving the estimation of GPP in combination with light use efficiency (LUE) models. Accurate estimation of LUE is essential for calculating GPP using remote sensing data and LUE models at regional and global scales. A promising method used for estimating LUE is the photochemical reflectance index (PRI = (R531－R570)/(R531 + R570), where R531 and R570 are reflectance at wavelengths 531 and 570 nm) through remote sensing. However, it has been documented that there are certain issues with PRI at the canopy scale, which need to be considered systematically. For this purpose, an improved tower-based automatic canopy multi-angle hyperspectral observation system was established at the Qianyanzhou flux station in China since January of 2013. In each 15-minute observation cycle, PRI was observed at four view zenith angles fixed at solar zenith angle and (37°, 47°, 57°) or (42°, 52°, 62°) in the azimuth angle range from 45° to 325° (defined from geodetic north). To improve the ability of directional PRI observation to track canopy LUE, the canopy is treated as two-big leaves, i.e. sunlit and shaded leaves. On the basis of a geometrical optical model, the observed canopy reflectance for each view angle is separated to four components, i.e. sunlit and shaded leaves and sunlit and shaded backgrounds. To determine the fractions of these four components at each view angle, three models based on different theories are tested for simulating the fraction of sunlit leaves. Finally, a ratio of canopy reflectance to leaf reflectance is used to represent the fraction of sunlit leaves, and the fraction of shaded leaves is calculated with the four-scale geometrical optical model. Thus, sunlit and shaded PRI are estimated using

Supervised Vicarious Calibration (SVC of Multi-Source Hyperspectral Remote-Sensing Data

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Anna Brook

2015-05-01

Full Text Available Introduced in 2011, the supervised vicarious calibration (SVC approach is a promising approach to radiometric calibration and atmospheric correction of airborne hyperspectral (HRS data. This paper presents a comprehensive study by which the SVC method has been systematically examined and a complete protocol for its practical execution has been established—along with possible limitations encountered during the campaign. The technique was applied to multi-sourced HRS data in order to: (1 verify the at-sensor radiometric calibration and (2 obtain radiometric and atmospheric correction coefficients. Spanning two select study sites along the southeast coast of France, data were collected simultaneously by three airborne sensors (AisaDUAL, AHS and CASI-1500i aboard two aircrafts (CASA of National Institute for Aerospace Technology INTA ES and DORNIER 228 of NERC-ARSF Centre UK. The SVC ground calibration site was assembled along sand dunes near Montpellier and the thematic data were acquired from other areas in the south of France (Salon-de-Provence, Marseille, Avignon and Montpellier on 28 October 2010 between 12:00 and 16:00 UTC. The results of this study confirm that the SVC method enables reliable inspection and, if necessary, in-situ fine radiometric recalibration of airborne hyperspectral data. Independent of sensor or platform quality, the SVC approach allows users to improve at-sensor data to obtain more accurate physical units and subsequently improved reflectance information. Flight direction was found to be important, whereas the flight altitude posed very low impact. The numerous rules and major outcomes of this experiment enable a new standard of atmospherically corrected data based on better radiometric output. Future research should examine the potential of SVC to be applied to super-and-hyperspectral data obtained from on-orbit sensors.

A New Algorithm for the On-Board Compression of Hyperspectral Images

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Raúl Guerra

2018-03-01

Full Text Available Hyperspectral sensors are able to provide information that is useful for many different applications. However, the huge amounts of data collected by these sensors are not exempt of drawbacks, especially in remote sensing environments where the hyperspectral images are collected on-board satellites and need to be transferred to the earth’s surface. In this situation, an efficient compression of the hyperspectral images is mandatory in order to save bandwidth and storage space. Lossless compression algorithms have been traditionally preferred, in order to preserve all the information present in the hyperspectral cube for scientific purposes, despite their limited compression ratio. Nevertheless, the increment in the data-rate of the new-generation sensors is making more critical the necessity of obtaining higher compression ratios, making it necessary to use lossy compression techniques. A new transform-based lossy compression algorithm, namely Lossy Compression Algorithm for Hyperspectral Image Systems (HyperLCA, is proposed in this manuscript. This compressor has been developed for achieving high compression ratios with a good compression performance at a reasonable computational burden. An extensive amount of experiments have been performed in order to evaluate the goodness of the proposed HyperLCA compressor using different calibrated and uncalibrated hyperspectral images from the AVIRIS and Hyperion sensors. The results provided by the proposed HyperLCA compressor have been evaluated and compared against those produced by the most relevant state-of-the-art compression solutions. The theoretical and experimental evidence indicates that the proposed algorithm represents an excellent option for lossy compressing hyperspectral images, especially for applications where the available computational resources are limited, such as on-board scenarios.

Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast

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Byoung-Kwan Cho

2013-09-01

Full Text Available Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum  of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an  of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat.

Tunable thin-film optical filters for hyperspectral microscopy

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Favreau, Peter F.; Rich, Thomas C.; Prabhat, Prashant; Leavesley, Silas J.

2013-02-01

Hyperspectral imaging was originally developed for use in remote sensing applications. More recently, it has been applied to biological imaging systems, such as fluorescence microscopes. The ability to distinguish molecules based on spectral differences has been especially advantageous for identifying fluorophores in highly autofluorescent tissues. A key component of hyperspectral imaging systems is wavelength filtering. Each filtering technology used for hyperspectral imaging has corresponding advantages and disadvantages. Recently, a new optical filtering technology has been developed that uses multi-layered thin-film optical filters that can be rotated, with respect to incident light, to control the center wavelength of the pass-band. Compared to the majority of tunable filter technologies, these filters have superior optical performance including greater than 90% transmission, steep spectral edges and high out-of-band blocking. Hence, tunable thin-film optical filters present optical characteristics that may make them well-suited for many biological spectral imaging applications. An array of tunable thin-film filters was implemented on an inverted fluorescence microscope (TE 2000, Nikon Instruments) to cover the full visible wavelength range. Images of a previously published model, GFP-expressing endothelial cells in the lung, were acquired using a charge-coupled device camera (Rolera EM-C2, Q-Imaging). This model sample presents fluorescently-labeled cells in a highly autofluorescent environment. Linear unmixing of hyperspectral images indicates that thin-film tunable filters provide equivalent spectral discrimination to our previous acousto-optic tunable filter-based approach, with increased signal-to-noise characteristics. Hence, tunable multi-layered thin film optical filters may provide greatly improved spectral filtering characteristics and therefore enable wider acceptance of hyperspectral widefield microscopy.

Improved analysis of Monascus pigments based on their pH-sensitive UV-Vis absorption and reactivity properties.

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Shi, Kan; Chen, Gong; Pistolozzi, Marco; Xia, Fenggeng; Wu, Zhenqiang

2016-09-01

Monascus pigments, a mixture of azaphilones mainly composed of red, orange and yellow pigments, are usually prepared in aqueous ethanol and analysed by ultraviolet-visible (UV-Vis) spectroscopy. The pH of aqueous ethanol used during sample preparation and analysis has never been considered a key parameter to control; however, this study shows that the UV-Vis spectra and colour characteristics of the six major pigments are strongly influenced by the pH of the solvent employed. In addition, the increase of solvent pH results in a remarkable increase of the amination reaction of orange pigments with amino compounds, and at higher pH (≥ 6.0) a significant amount of orange pigment derivatives rapidly form. The consequent impact of these pH-sensitive properties on pigment analysis is further discussed. Based on the presented results, we propose that the sample preparation and analysis of Monascus pigments should be uniformly performed at low pH (≤ 2.5) to avoid variations of UV-Vis spectra and the creation of artefacts due to the occurrence of amination reactions, and ensure an accurate analysis that truly reflects pigment characteristics in the samples.

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

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Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

2012-01-01

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo

Science.gov (United States)

Peller, Joseph; Thompson, Kyle J.; Siddiqui, Imran; Martinie, John; Iannitti, David A.; Trammell, Susan R.

2017-02-01

Pancreatic cancer is the fourth leading cause of cancer death in the US. Currently, surgery is the only treatment that offers a chance of cure, however, accurately identifying tumor margins in real-time is difficult. Research has demonstrated that optical spectroscopy can be used to distinguish between healthy and diseased tissue. The design of a single-pixel imaging system for cancer detection is discussed. The system differentiates between healthy and diseased tissue based on differences in the optical reflectance spectra of these regions. In this study, pancreatic tissue samples from 6 patients undergoing Whipple procedures are imaged with the system (total number of tissue sample imaged was N=11). Regions of healthy and unhealthy tissue are determined based on SAM analysis of these spectral images. Hyperspectral imaging results are then compared to white light imaging and histological analysis. Cancerous regions were clearly visible in the hyperspectral images. Margins determined via spectral imaging were in good agreement with margins identified by histology, indicating that hyperspectral imaging system can differentiate between healthy and diseased tissue. After imaging the system was able to detect cancerous regions with a sensitivity of 74.50±5.89% and a specificity of 75.53±10.81%. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and assistance with cancer diagnosis and staging.

Hyperspectral water quality retrieval model: taking Malaysia inshore sea area as an example

Science.gov (United States)

Cui, Tingwei; Zhang, Jie; Ma, Yi; Li, Jing; Lim, Boonleong; Roslinah, Samad

2007-11-01

Remote sensing technique provides the possibility of rapid and synchronous monitoring in a large area of the water quality, which is an important element for the aquatic ecosystem quality assessment of islands and coastal zones, especially for the nearshore and tourism sea area. Tioman Island of Malaysia is regarded as one of ten of the best islands in the world and attracts tourists from all over the world for its clear sea, beautiful seashore and charming scenery. In this paper, on the basis of in situ dataset in the study area, distribution discipline of water quality parameters is analyzed to find that phytoplankton pigment, rather than suspended sediment is the main water quality parameter in the study area; seawater there is clean but not very oligotrophic; seawater spectra contains distinct features. Then water quality hyperspectral retrieval models are developed based on in situ data to calculate the chlorophyll a concentration ([chl-a]), transparency (SD) with satisfactory performance. It's suggested that model precision should be validated further using more in-situ data.

High-resolution hyperspectral ground mapping for robotic vision

Science.gov (United States)

Neuhaus, Frank; Fuchs, Christian; Paulus, Dietrich

2018-04-01

Recently released hyperspectral cameras use large, mosaiced filter patterns to capture different ranges of the light's spectrum in each of the camera's pixels. Spectral information is sparse, as it is not fully available in each location. We propose an online method that avoids explicit demosaicing of camera images by fusing raw, unprocessed, hyperspectral camera frames inside an ego-centric ground surface map. It is represented as a multilayer heightmap data structure, whose geometry is estimated by combining a visual odometry system with either dense 3D reconstruction or 3D laser data. We use a publicly available dataset to show that our approach is capable of constructing an accurate hyperspectral representation of the surface surrounding the vehicle. We show that in many cases our approach increases spatial resolution over a demosaicing approach, while providing the same amount of spectral information.

Demystifying autofluorescence with excitation scanning hyperspectral imaging

Science.gov (United States)

Deal, Joshua; Harris, Bradley; Martin, Will; Lall, Malvika; Lopez, Carmen; Rider, Paul; Boudreaux, Carole; Rich, Thomas; Leavesley, Silas J.

2018-02-01

Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitationscanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states.

UV-B affects the immune system and promotes nuclear abnormalities in pigmented and non-pigmented bullfrog tadpoles.

Science.gov (United States)

Franco-Belussi, Lilian; Fanali, Lara Zácari; De Oliveira, Classius

2018-03-01

Ultra-Violet (UV) radiation is a stressor of the immune system and causes DNA damage. Leukocytes can change in response to environmental changes in anurans, making them an important biomarker of stressful situations. The initial barrier against UV in ectothermic animals is melanin-containing cells in skin and in their internal organs. Here, we tested the effects of UV exposure on immune cells and DNA integrity in pigmented and non-pigmented tadpoles of Lithobates catesbeianus. We used an inflammation model with lipopolysaccharide (LPS) of Escherichia coli to test synergic effects of UV and LPS. We tested the following hypotheses: 1) DNA damage caused by UV will be more pronounced in non-pigmented than in pigmented animals; 2) LPS increases leukocytes in both pigmented and non-pigmented animals by systemic inflammation; 3) The combined LPS and UV exposure will decrease the number of leukocytes. We found that the frequency of immune cells differed between pigmented and non-pigmented tadpoles. UV exposure increased mast cells and DNA damage in erythrocytes in both pigmented and non-pigmented tadpoles, while leukocytes decreased after UV exposure. Non-pigmented tadpoles experienced DNA damage and a lower lymphocyte count earlier than pigmented tadpoles. UV altered immune cells likely as a consequence of local and systemic inflammation. These alterations were less severe in pigmented than in non-pigmented animals. UV and LPS increased internal melanin in pigmented tadpoles, which were correlated with DNA damage and leukocytes. Here, we described for the first time the effects of UV and LPS in immune cells of pigmented and non-pigmented tadpoles. In addition, we demonstrated that internal melanin in tadpoles help in these defenses, since leukocyte responses were faster in non-pigmented animals, supporting the hypothesis that melanin is involved in the initial innate immune response. Copyright © 2018 Elsevier B.V. All rights reserved.

New features to the night sky radiance model illumina: Hyperspectral support, improved obstacles and cloud reflection

Science.gov (United States)

Aubé, M.; Simoneau, A.

2018-05-01

Illumina is one of the most physically detailed artificial night sky brightness model to date. It has been in continuous development since 2005 [1]. In 2016-17, many improvements were made to the Illumina code including an overhead cloud scheme, an improved blocking scheme for subgrid obstacles (trees and buildings), and most importantly, a full hyperspectral modeling approach. Code optimization resulted in significant reduction in execution time enabling users to run the model on standard personal computers for some applications. After describing the new schemes introduced in the model, we give some examples of applications for a peri-urban and a rural site both located inside the International Dark Sky reserve of Mont-Mégantic (QC, Canada).

Image Segmentation of Hyperspectral Imagery

National Research Council Canada - National Science Library

Wellman, Mark

2003-01-01

.... Army tactical applications. An important tactical application of infrared (IR) hyperspectral imagery is the detection of low-contrast targets, including those targets that may employ camouflage, concealment, and deception (CCD) techniques 1, 2...

Manifold learning based feature extraction for classification of hyper-spectral data

CSIR Research Space (South Africa)

Lunga, D

2013-08-01

Full Text Available Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. Hyperspectral image data...

Blind estimation of blur in hyperspectral images

Science.gov (United States)

Zhang, Mo; Vozel, Benoit; Chehdi, Kacem; Uss, Mykhail; Abramov, Sergey; Lukin, Vladimir

2017-10-01

Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present on the original image, blind restoration methods can only be considered. By blind, we mean absolutely no knowledge neither of the blur point spread function (PSF) nor the original latent channel and the noise level. In this study, we address the blind restoration of the degraded channels component-wise, according to a sequential scheme. For each degraded channel, the sequential scheme estimates the blur point spread function (PSF) in a first stage and deconvolves the degraded channel in a second and final stage by means of using the PSF previously estimated. We propose a new component-wise blind method for estimating effectively and accurately the blur point spread function. This method follows recent approaches suggesting the detection, selection and use of sufficiently salient edges in the current processed channel for supporting the regularized blur PSF estimation. Several modifications are beneficially introduced in our work. A new selection of salient edges through thresholding adequately the cumulative distribution of their corresponding gradient magnitudes is introduced. Besides, quasi-automatic and spatially adaptive tuning of the involved regularization parameters is considered. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from and four representative edge-sparsifying regularized methods of the literature already assessed in a previous work. Our attention is mainly paid to the objective analysis (via ݈l1-norm) of the blur PSF error estimation accuracy. The tests are performed on a synthetic hyperspectral image. This synthetic hyperspectral image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic spatial

Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves.

Science.gov (United States)

Junker, Laura Verena; Ensminger, Ingo

2016-06-01

The ability of plants to sequester carbon is highly variable over the course of the year and reflects seasonal variation in photosynthetic efficiency. This seasonal variation is most prominent during autumn, when leaves of deciduous tree species such as sugar maple (Acer saccharum Marsh.) undergo senescence, which is associated with downregulation of photosynthesis and a change of leaf color. The remote sensing of leaf color by spectral reflectance measurements and digital repeat images is increasingly used to improve models of growing season length and seasonal variation in carbon sequestration. Vegetation indices derived from spectral reflectance measurements and digital repeat images might not adequately reflect photosynthetic efficiency of red-senescing tree species during autumn due to the changes in foliar pigment content associated with autumn phenology. In this study, we aimed to assess how effectively several widely used vegetation indices capture autumn phenology and reflect the changes in physiology and photosynthetic pigments during autumn. Chlorophyll fluorescence and pigment content of green, yellow, orange and red leaves were measured to represent leaf senescence during autumn and used as a reference to validate and compare vegetation indices derived from leaf-level spectral reflectance measurements and color analysis of digital images. Vegetation indices varied in their suitability to track the decrease of photosynthetic efficiency and chlorophyll content despite increasing anthocyanin content. Commonly used spectral reflectance indices such as the normalized difference vegetation index and photochemical reflectance index showed major constraints arising from a limited representation of gradual decreases in chlorophyll content and an influence of high foliar anthocyanin levels. The excess green index and green-red vegetation index were more suitable to assess the process of senescence. Similarly, digital image analysis revealed that vegetation

Carbachol-mediated pigment granule dispersion in retinal pigment epithelium requires Ca2+ and calcineurin

OpenAIRE

Johnson, Adam S; Garc?a, Dana M

2007-01-01

Abstract Background Inside bluegill (Lepomis macrochirus) retinal pigment epithelial cells, pigment granules move in response to extracellular signals. During the process of aggregation, pigment motility is directed toward the cell nucleus; in dispersion, pigment is directed away from the nucleus and into long apical processes. A number of different chemicals have been found to initiate dispersion, and carbachol (an acetylcholine analog) is one example. Previous research indicates that the ca...

[Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

Science.gov (United States)

Wang, Li-Wen; Wei, Ya-Xing

2013-10-01

Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

Directory of Open Access Journals (Sweden)

Demidova Liliya

2017-01-01

Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

Panoramic, Macro and Micro Multispectral Imaging: An Affordable System for Mapping Pigments on Artworks

Directory of Open Access Journals (Sweden)

Antonino Cosentino

2015-07-01

Full Text Available Multispectral imaging systems are used in art examinations to map and identify pigments, binders and areas of retouching. A monochromatic camera is combined with an appropriate wavelength selection system and acquires a variable number of spectral images of a scene. These images are then stacked into a reflectance imaging cube to reconstruct reflectance spectra from each of the images’ pixels. This paper presents an affordable multispectral imaging system composed of a monochromatic CCD camera and a set of only 12 interference filters for mapping pigments on works of art and for the tentative identification of such pigments. This work demonstrates the versatility of this set-up, a versatility enabling it to be applied to different tasks, involving examination and documentation of objects of varying size. Use of this multispectral camera for both panoramic and macro photography is discussed, together with the possibilities facilitated from the coupling of the system to a stereomicroscope and a compound microscope. This system is of particular interest for the cultural heritage sector because of its hardware simplicity and acquisition speed, as well as its lightness and small dimensions.

Bathymetry from fusion of airborne hyperspectral and laser data

Science.gov (United States)

Kappus, Mary E.; Davis, Curtiss O.; Rhea, W. Joseph

1998-10-01

Airborne hyperspectral and nadir-viewing laser data can be combined to ascertain shallow water bathymetry. The combination emphasizes the advances and overcomes the disadvantages of each method used alone. For laser systems, both the hardware and software for obtaining off-nadir measurement are complicated and expensive, while for the nadir view the conversion of laser pulse travel time to depth is straightforward. The hyperspectral systems can easily collect data in a full swath, but interpretation for water depth requires careful calibration and correction for transmittance through the atmosphere and water. Relative depths are apparent in displays of several subsets of hyperspectral data, for example, single blue-green wavelengths, endmembers that represent the pure water component of the data, or ratios of deep to shallow water endmembers. A relationship between one of these values and the depth measured by the aligned nadir laser can be determined, and then applied to the rest of the swath to obtain depth in physical units for the entire area covered. We demonstrate this technique using bathymetric charts as a proxy for laser data, and hyperspectral data taken by AVIRIS over Lake Tahoe and Key West.

Hyperspectral material identification on radiance data using single-atmosphere or multiple-atmosphere modeling

Science.gov (United States)

Mariano, Adrian V.; Grossmann, John M.

2010-11-01

Reflectance-domain methods convert hyperspectral data from radiance to reflectance using an atmospheric compensation model. Material detection and identification are performed by comparing the compensated data to target reflectance spectra. We introduce two radiance-domain approaches, Single atmosphere Adaptive Cosine Estimator (SACE) and Multiple atmosphere ACE (MACE) in which the target reflectance spectra are instead converted into sensor-reaching radiance using physics-based models. For SACE, known illumination and atmospheric conditions are incorporated in a single atmospheric model. For MACE the conditions are unknown so the algorithm uses many atmospheric models to cover the range of environmental variability, and it approximates the result using a subspace model. This approach is sometimes called the invariant method, and requires the choice of a subspace dimension for the model. We compare these two radiance-domain approaches to a Reflectance-domain ACE (RACE) approach on a HYDICE image featuring concealed materials. All three algorithms use the ACE detector, and all three techniques are able to detect most of the hidden materials in the imagery. For MACE we observe a strong dependence on the choice of the material subspace dimension. Increasing this value can lead to a decline in performance.

Underwater Hyperspectral Imaging (UHI) for Assessing the Coverage of Drill Cuttings on Benthic Habitats

Science.gov (United States)

Erdal, I.; Sandvik Aas, L. M.; Cochrane, S.; Ekehaug, S.; Hansen, I. M.

2016-02-01

Larger-scale mapping of seabed areas requires improved methods in order to obtain effective and sound marine management. The state of the art for visual surveys today involves video transects, which is a proven, yet time consuming and subjective method. Underwater hyperspectral imaging (UHI) utilizes high color sensitive information in the visible light reflected from objects on the seafloor to automatically identify seabed organisms and other objects of interest (OOI). A spectral library containing optical fingerprints of a range of OOI's are used in the classification. The UHI is a push-broom hyperspectral camera utilizing a state of the art CMOS sensor ensuring high sensitivity and low noise levels. Dedicated lamps illuminate the imaging area of the seafloor. Specialized software is used both for processing raw data and for geo-localization and OOI identification. The processed hyperspectral image are used as a reference when extracting new spectral data for OOI's to the spectral library. By using the spectral library in classification algorithms, large sea floor areas can automatically be classified. Recent advantages in UHI classification includes mapping of areas affected by drill cuttings. Tools for automated classification of seabed that have a different bottom composition than adjacent baseline areas are under development. Tests have been applied to a transect in gradient from the drilling hole to baseline seabed. Some areas along the transect were identified as different compared to baseline seabed. The finding was supported by results from traditional seabed mapping methods. We propose that this can be a useful tool for tomorrows environmental mapping and monitoring of drill sites.

Dimensionality Reduction for Hyperspectral Data Based on Class-Aware Tensor Neighborhood Graph and Patch Alignment.

Science.gov (United States)

Gao, Yang; Wang, Xuesong; Cheng, Yuhu; Wang, Z Jane

2015-08-01

To take full advantage of hyperspectral information, to avoid data redundancy and to address the curse of dimensionality concern, dimensionality reduction (DR) becomes particularly important to analyze hyperspectral data. Exploring the tensor characteristic of hyperspectral data, a DR algorithm based on class-aware tensor neighborhood graph and patch alignment is proposed here. First, hyperspectral data are represented in the tensor form through a window field to keep the spatial information of each pixel. Second, using a tensor distance criterion, a class-aware tensor neighborhood graph containing discriminating information is obtained. In the third step, employing the patch alignment framework extended to the tensor space, we can obtain global optimal spectral-spatial information. Finally, the solution of the tensor subspace is calculated using an iterative method and low-dimensional projection matrixes for hyperspectral data are obtained accordingly. The proposed method effectively explores the spectral and spatial information in hyperspectral data simultaneously. Experimental results on 3 real hyperspectral datasets show that, compared with some popular vector- and tensor-based DR algorithms, the proposed method can yield better performance with less tensor training samples required.

Hyperspectral Imaging Sensors and the Marine Coastal Zone

Science.gov (United States)

Richardson, Laurie L.

2000-01-01

Hyperspectral imaging sensors greatly expand the potential of remote sensing to assess, map, and monitor marine coastal zones. Each pixel in a hyperspectral image contains an entire spectrum of information. As a result, hyperspectral image data can be processed in two very different ways: by image classification techniques, to produce mapped outputs of features in the image on a regional scale; and by use of spectral analysis of the spectral data embedded within each pixel of the image. The latter is particularly useful in marine coastal zones because of the spectral complexity of suspended as well as benthic features found in these environments. Spectral-based analysis of hyperspectral (AVIRIS) imagery was carried out to investigate a marine coastal zone of South Florida, USA. Florida Bay is a phytoplankton-rich estuary characterized by taxonomically distinct phytoplankton assemblages and extensive seagrass beds. End-member spectra were extracted from AVIRIS image data corresponding to ground-truth sample stations and well-known field sites. Spectral libraries were constructed from the AVIRIS end-member spectra and used to classify images using the Spectral Angle Mapper (SAM) algorithm, a spectral-based approach that compares the spectrum, in each pixel of an image with each spectrum in a spectral library. Using this approach different phytoplankton assemblages containing diatoms, cyanobacteria, and green microalgae, as well as benthic community (seagrasses), were mapped.

Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants

Science.gov (United States)

Atherton, Daniel

Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p potato plants.

Long anterior zonules and pigment dispersion.

Science.gov (United States)

Moroi, Sayoko E; Lark, Kurt K; Sieving, Paul A; Nouri-Mahdavi, Kouros; Schlötzer-Schrehardt, Ursula; Katz, Gregory J; Ritch, Robert

2003-12-01

To describe pigment dispersion associated with long anterior zonules. Multicenter observational case series. Fifteen patients, seven of whom were treated for glaucoma or ocular hypertension, were identified with long anterior zonules and pigment dispersion. Transmission electron microscopy was performed on one anterior capsule specimen. All patients had anterior zonules that inserted centrally on the lens capsule. Signs of pigment dispersion included corneal endothelial pigmentation, loss of the pupillary ruff, and variable trabecular meshwork pigmentation. Ultrasound biomicroscopy verified the lack of posterior iris insertion and concavity. There was no exfoliation material. Transmission electron microscopy showed zonular lamellae with adherent pigment granules, and no exfoliation material. Long anterior zonules inserted onto the central lens capsule may cause mechanical disruption of the pigment epithelium at the pupillary ruff and central iris leading to pigment dispersion.

Hyperspectral remote sensing of vegetation

Science.gov (United States)

Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

2011-01-01

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

Technical note: comparing von Luschan skin color tiles and modern spectrophotometry for measuring human skin pigmentation.

Science.gov (United States)

Swiatoniowski, Anna K; Quillen, Ellen E; Shriver, Mark D; Jablonski, Nina G

2013-06-01

Prior to the introduction of reflectance spectrophotometry into anthropological field research during the 1950s, human skin color was most commonly classified by visual skin color matching using the von Luschan tiles, a set of 36 standardized, opaque glass tiles arranged in a chromatic scale. Our goal was to establish a conversion formula between the tile-based color matching method and modern reflectance spectrophotometry to make historical and contemporary data comparable. Skin pigmentation measurements were taken on the forehead, inner upper arms, and backs of the hands using both the tiles and a spectrophotometer on 246 participants showing a broad range of skin pigmentation. From these data, a second-order polynomial conversion formula was derived by jackknife analysis to estimate melanin index (M-index) based on tile values. This conversion formula provides a means for comparing modern data to von Luschan tile measurements recorded in historical reports. This is particularly important for populations now extinct, extirpated, or admixed for which tile-based measures of skin pigmentation are the only data available. Copyright © 2013 Wiley Periodicals, Inc.

A FPGA implementation for linearly unmixing a hyperspectral image using OpenCL

Science.gov (United States)

Guerra, Raúl; López, Sebastián.; Sarmiento, Roberto

2017-10-01

Hyperspectral imaging systems provide images in which single pixels have information from across the electromagnetic spectrum of the scene under analysis. These systems divide the spectrum into many contiguos channels, which may be even out of the visible part of the spectra. The main advantage of the hyperspectral imaging technology is that certain objects leave unique fingerprints in the electromagnetic spectrum, known as spectral signatures, which allow to distinguish between different materials that may look like the same in a traditional RGB image. Accordingly, the most important hyperspectral imaging applications are related with distinguishing or identifying materials in a particular scene. In hyperspectral imaging applications under real-time constraints, the huge amount of information provided by the hyperspectral sensors has to be rapidly processed and analysed. For such purpose, parallel hardware devices, such as Field Programmable Gate Arrays (FPGAs) are typically used. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies which can be used to perform the implementation of the desired algorithms in the specific device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level synthesis design language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the Fast Algorithm for Linearly Unmixing Hyperspectral Images (FUN) has been implemented into a Bitware Stratix V Altera FPGA using OpenCL. The obtained results demonstrate the suitability of OpenCL as a viable design methodology for quickly creating efficient FPGAs designs for real-time hyperspectral imaging applications.

Photosynthetic Pigments in Diatoms.

Science.gov (United States)

Kuczynska, Paulina; Jemiola-Rzeminska, Malgorzata; Strzalka, Kazimierz

2015-09-16

Photosynthetic pigments are bioactive compounds of great importance for the food, cosmetic, and pharmaceutical industries. They are not only responsible for capturing solar energy to carry out photosynthesis, but also play a role in photoprotective processes and display antioxidant activity, all of which contribute to effective biomass and oxygen production. Diatoms are organisms of a distinct pigment composition, substantially different from that present in plants. Apart from light-harvesting pigments such as chlorophyll a, chlorophyll c, and fucoxanthin, there is a group of photoprotective carotenoids which includes β-carotene and the xanthophylls, diatoxanthin, diadinoxanthin, violaxanthin, antheraxanthin, and zeaxanthin, which are engaged in the xanthophyll cycle. Additionally, some intermediate products of biosynthetic pathways have been identified in diatoms as well as unusual pigments, e.g., marennine. Marine algae have become widely recognized as a source of unique bioactive compounds for potential industrial, pharmaceutical, and medical applications. In this review, we summarize current knowledge on diatom photosynthetic pigments complemented by some new insights regarding their physico-chemical properties, biological role, and biosynthetic pathways, as well as the regulation of pigment level in the cell, methods of purification, and significance in industries.

Photosynthetic Pigments in Diatoms

Directory of Open Access Journals (Sweden)

Paulina Kuczynska

2015-09-01

Full Text Available Photosynthetic pigments are bioactive compounds of great importance for the food, cosmetic, and pharmaceutical industries. They are not only responsible for capturing solar energy to carry out photosynthesis, but also play a role in photoprotective processes and display antioxidant activity, all of which contribute to effective biomass and oxygen production. Diatoms are organisms of a distinct pigment composition, substantially different from that present in plants. Apart from light-harvesting pigments such as chlorophyll a, chlorophyll c, and fucoxanthin, there is a group of photoprotective carotenoids which includes β-carotene and the xanthophylls, diatoxanthin, diadinoxanthin, violaxanthin, antheraxanthin, and zeaxanthin, which are engaged in the xanthophyll cycle. Additionally, some intermediate products of biosynthetic pathways have been identified in diatoms as well as unusual pigments, e.g., marennine. Marine algae have become widely recognized as a source of unique bioactive compounds for potential industrial, pharmaceutical, and medical applications. In this review, we summarize current knowledge on diatom photosynthetic pigments complemented by some new insights regarding their physico-chemical properties, biological role, and biosynthetic pathways, as well as the regulation of pigment level in the cell, methods of purification, and significance in industries.

High-resolution optical coherence tomography, autofluorescence, and infrared reflectance imaging in Sjögren reticular dystrophy.

Science.gov (United States)

Schauwvlieghe, Pieter-Paul; Torre, Kara Della; Coppieters, Frauke; Van Hoey, Anneleen; De Baere, Elfride; De Zaeytijd, Julie; Leroy, Bart P; Brodie, Scott E

2013-01-01

To describe the phenotype of three cases of Sjögren reticular dystrophy in detail, including high-resolution optical coherence tomography, autofluorescence imaging, and near-infrared reflectance imaging. Two unrelated teenagers were independently referred for ophthalmologic evaluation. Both underwent a full ophthalmologic workup, including electrophysiologic and extensive imaging with spectral-domain optical coherence tomography, autofluorescence imaging, and near-infrared reflectance imaging. In addition, mutation screening of ABCA4, PRPH2, and the mitochondrial tRNA gene was performed in Patient 1. Subsequently, the teenage sister of Patient 2 was examined. Strikingly similar phenotypes were present in these three patients. Fundoscopy showed bilateral foveal pigment alterations, and a lobular network of deep retinal, pigmented deposits throughout the posterior pole, tapering toward the midperiphery, with relative sparing of the immediate perifoveal macula and peripapillary area. This network is mildly to moderately hyperautofluorescent on autofluorescence and bright on near-infrared reflectance imaging. Optical coherence tomography showed abnormalities of the retinal pigment epithelium-Bruch membrane complex, photoreceptor outer segments, and photoreceptor inner/outer segment interface. The results of retinal function test were entirely normal. No molecular cause was detected in Patient 1. Imaging suggested that the lobular network of deep retinal deposits in Sjögren reticular dystrophy is the result of accumulation of both pigment and lipofuscin between photoreceptors and retinal pigment epithelium, as well as within the retinal pigment epithelium.

High Angular Resolution Measurements of the Anisotropy of Reflectance of Sea Ice and Snow

Science.gov (United States)

Goyens, C.; Marty, S.; Leymarie, E.; Antoine, D.; Babin, M.; Bélanger, S.

2018-01-01

We introduce a new method to determine the anisotropy of reflectance of sea ice and snow at spatial scales from 1 m2 to 80 m2 using a multispectral circular fish-eye radiance camera (CE600). The CE600 allows measuring radiance simultaneously in all directions of a hemisphere at a 1° angular resolution. The spectral characteristics of the reflectance and its dependency on illumination conditions obtained from the camera are compared to those obtained with a hyperspectral field spectroradiometer manufactured by Analytical Spectral Device, Inc. (ASD). Results confirm the potential of the CE600, with the suggested measurement setup and data processing, to measure commensurable sea ice and snow hemispherical-directional reflectance factor, HDRF, values. Compared to the ASD, the reflectance anisotropy measured with the CE600 provides much higher resolution in terms of directional reflectance (N = 16,020). The hyperangular resolution allows detecting features that were overlooked using the ASD due to its limited number of measurement angles (N = 25). This data set of HDRF further documents variations in the anisotropy of the reflectance of snow and ice with the geometry of observation and illumination conditions and its spectral and spatial scale dependency. Finally, in order to reproduce the hyperangular CE600 reflectance measurements over the entire 400-900 nm spectral range, a regression-based method is proposed to combine the ASD and CE600 measurements. Results confirm that both instruments may be used in synergy to construct a hyperangular and hyperspectral snow and ice reflectance anisotropy data set.

Single crystal X-ray structure of the artists' pigment zinc yellow

Science.gov (United States)

Simonsen, Kim Pilkjær; Christiansen, Marie Bitsch; Vinum, Morten Gotthold; Sanyova, Jana; Bendix, Jesper

2017-08-01

The artists' pigment zinc yellow is in general described as a complex potassium zinc chromate with the empirical formula 4ZnCrO4·K2O·3H2O. Even though the pigment has been in use since the second half of the 19th century also in large-scale industrial applications, the exact structure had hitherto been unknown. In this work, zinc yellow was synthesised by precipitation from an aqueous solution of zinc nitrate and potassium chromate under both neutral and basic conditions, and the products were compared with the pigment used in industrial paints. Analyses by Raman microscopy (MRS), scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), and powder X-ray diffraction (PXRD), showed that the synthesised products and the industrial pigment were identical. Single-crystal X-ray crystallography determined the structure of zinc yellow as KZn2(CrO4)2(H2O)(OH) or as KZn2(CrO4)2(H3O2) emphasizing the μ-H3O2- moiety. Notably, the zinc yellow is isostructural to the recently structurally characterized cadmium analog and both belong to the natrochalcite structure type.

Quantitative Comparison of the Variability in Observed and Simulated Shortwave Reflectance

Science.gov (United States)

Roberts, Yolanda, L.; Pilewskie, P.; Kindel, B. C.; Feldman, D. R.; Collins, W. D.

2013-01-01

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is a climate observation system that has been designed to monitor the Earth's climate with unprecedented absolute radiometric accuracy and SI traceability. Climate Observation System Simulation Experiments (OSSEs) have been generated to simulate CLARREO hyperspectral shortwave imager measurements to help define the measurement characteristics needed for CLARREO to achieve its objectives. To evaluate how well the OSSE-simulated reflectance spectra reproduce the Earth s climate variability at the beginning of the 21st century, we compared the variability of the OSSE reflectance spectra to that of the reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Principal component analysis (PCA) is a multivariate decomposition technique used to represent and study the variability of hyperspectral radiation measurements. Using PCA, between 99.7%and 99.9%of the total variance the OSSE and SCIAMACHY data sets can be explained by subspaces defined by six principal components (PCs). To quantify how much information is shared between the simulated and observed data sets, we spectrally decomposed the intersection of the two data set subspaces. The results from four cases in 2004 showed that the two data sets share eight (January and October) and seven (April and July) dimensions, which correspond to about 99.9% of the total SCIAMACHY variance for each month. The spectral nature of these shared spaces, understood by examining the transformed eigenvectors calculated from the subspace intersections, exhibit similar physical characteristics to the original PCs calculated from each data set, such as water vapor absorption, vegetation reflectance, and cloud reflectance.

High-emulation mask recognition with high-resolution hyperspectral video capture system

Science.gov (United States)

Feng, Jiao; Fang, Xiaojing; Li, Shoufeng; Wang, Yongjin

2014-11-01

We present a method for distinguishing human face from high-emulation mask, which is increasingly used by criminals for activities such as stealing card numbers and passwords on ATM. Traditional facial recognition technique is difficult to detect such camouflaged criminals. In this paper, we use the high-resolution hyperspectral video capture system to detect high-emulation mask. A RGB camera is used for traditional facial recognition. A prism and a gray scale camera are used to capture spectral information of the observed face. Experiments show that mask made of silica gel has different spectral reflectance compared with the human skin. As multispectral image offers additional spectral information about physical characteristics, high-emulation mask can be easily recognized.

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

OpenAIRE

Julie Transon; Raphaël d’Andrimont; Alexandre Maugnard; Pierre Defourny

2018-01-01

In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This st...

Cone pigment polymorphism in New World monkeys: are all pigments created equal?

Science.gov (United States)

Rowe, Mickey P; Jacobs, Gerald H

2004-01-01

Most platyrrhine monkeys have a triallelic M/L opsin gene polymorphism that underlies significant individual variations in color vision. A survey of the frequencies of these polymorphic genes suggests that the three alleles occur with equal frequency among squirrel monkeys (subfamily Cebinae), but are not equally frequent in a number of species from the subfamily Callitrichinae. This departure from equal frequency in the Callitrichids should slightly increase the ratio of dichromats to trichromats in the population and significantly alter the relative representation of the three possible dichromatic and trichromatic phenotypes. A particular feature of the inequality is that it leads to a relative increase in the number of trichromats whose M/L pigments have the largest possible spectral separation. To assess whether these trichromatic phenotypes are equally well equipped to make relevant visual discriminations, psychophysical experiments were run on human observers. A technique involving the functional substitution of photopigments was used to simulate the discrimination between fruits among a background of leaves. The goal of the simulation was to reproduce in the cones of human observers excitations equivalent to those produced in monkey cones as the animals view fruit. Three different viewing conditions were examined involving variations in the relative luminances of fruit and leaves and the spectrum of the illuminant. In all cases, performance was best for simulated trichromacies including M/L pigments with the largest spectral separation. Thus, the inequality of opsin gene frequency in Callitrichid monkeys may reflect adaptive pressures.

Bread Water Content Measurement Based on Hyperspectral Imaging

DEFF Research Database (Denmark)

Liu, Zhi; Møller, Flemming

2011-01-01

Water content is one of the most important properties of the bread for tasting assesment or store monitoring. Traditional bread water content measurement methods mostly are processed manually, which is destructive and time consuming. This paper proposes an automated water content measurement...... for bread quality based on near-infrared hyperspectral imaging against the conventional manual loss-in-weight method. For this purpose, the hyperspectral components unmixing technology is used for measuring the water content quantitatively. And the definition on bread water content index is presented...

The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo—A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data

Directory of Open Access Journals (Sweden)

Sandra Jakob

2017-01-01

Full Text Available Drone-borne hyperspectral imaging is a new and promising technique for fast and precise acquisition, as well as delivery of high-resolution hyperspectral data to a large variety of end-users. Drones can overcome the scale gap between field and air-borne remote sensing, thus providing high-resolution and multi-temporal data. They are easy to use, flexible and deliver data within cm-scale resolution. So far, however, drone-borne imagery has prominently and successfully been almost solely used in precision agriculture and photogrammetry. Drone technology currently mainly relies on structure-from-motion photogrammetry, aerial photography and agricultural monitoring. Recently, a few hyperspectral sensors became available for drones, but complex geometric and radiometric effects complicate their use for geology-related studies. Using two examples, we first show that precise corrections are required for any geological mapping. We then present a processing toolbox for frame-based hyperspectral imaging systems adapted for the complex correction of drone-borne hyperspectral imagery. The toolbox performs sensor- and platform-specific geometric distortion corrections. Furthermore, a topographic correction step is implemented to correct for rough terrain surfaces. We recommend the c-factor-algorithm for geological applications. To our knowledge, we demonstrate for the first time the applicability of the corrected dataset for lithological mapping and mineral exploration.

Portable hyperspectral device as a valuable tool for the detection of protective agents applied on hystorical buildings

Science.gov (United States)

Vettori, S.; Pecchioni, E.; Camaiti, M.; Garfagnoli, F.; Benvenuti, M.; Costagliola, P.; Moretti, S.

2012-04-01

In the recent past, a wide range of protective products (in most cases, synthetic polymers) have been applied to the surfaces of ancient buildings/artefacts to preserve them from alteration [1]. The lack of a detailed mapping of the permanence and efficacy of these treatments, in particular when applied on large surfaces such as building facades, may be particularly noxious when new restoration treatments are needed and the best choice of restoration protocols has to be taken. The presence of protective compounds on stone surfaces may be detected in laboratory by relatively simple diagnostic tests, which, however, normally require invasive (or micro-invasive) sampling methodologies and are time-consuming, thus limiting their use only to a restricted number of samples and sampling sites. On the contrary, hyperspectral sensors are rapid, non-invasive and non-destructive tools capable of analyzing different materials on the basis of their different patterns of absorption at specific wavelengths, and so particularly suitable for the field of cultural heritage [2,3]. In addition, they can be successfully used to discriminate between inorganic (i.e. rocks and minerals) and organic compounds, as well as to acquire, in short times, many spectra and compositional maps at relatively low costs. In this study we analyzed a number of stone samples (Carrara Marble and biogenic calcarenites - "Lecce Stone" and "Maastricht Stone"-) after treatment of their surfaces with synthetic polymers (synthetic wax, acrylic, perfluorinated and silicon based polymers) of common use in conservation-restoration practice. The hyperspectral device used for this purpose was ASD FieldSpec FR Pro spectroradiometer, a portable, high-resolution instrument designed to acquire Visible and Near-Infrared (VNIR: 350-1000 nm) and Short-Wave Infrared (SWIR: 1000-2500 nm) punctual reflectance spectra with a rapid data collection time (about 0.1 s for each spectrum). The reflectance spectra so far obtained in

a Hyperspectral Image Classification Method Using Isomap and Rvm

Science.gov (United States)

Chang, H.; Wang, T.; Fang, H.; Su, Y.

2018-04-01

Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes' ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.

Activation of muscarinic acetylcholine receptors elicits pigment granule dispersion in retinal pigment epithelium isolated from bluegill.

Science.gov (United States)

González, Alfredo; Crittenden, Elizabeth L; García, Dana M

2004-07-13

In fish, melanin pigment granules in the retinal pigment epithelium disperse into apical projections as part of the suite of responses the eye makes to bright light conditions. This pigment granule dispersion serves to reduce photobleaching and occurs in response to neurochemicals secreted by the retina. Previous work has shown that acetylcholine may be involved in inducing light-adaptive pigment dispersion. Acetylcholine receptors are of two main types, nicotinic and muscarinic. Muscarinic receptors are in the G-protein coupled receptor superfamily, and five different muscarinic receptors have been molecularly cloned in human. These receptors are coupled to adenylyl cyclase, calcium mobilization and ion channel activation. To determine the receptor pathway involved in eliciting pigment granule migration, we isolated retinal pigment epithelium from bluegill and subjected it to a battery of cholinergic agents. The general cholinergic agonist carbachol induces pigment granule dispersion in isolated retinal pigment epithelium. Carbachol-induced pigment granule dispersion is blocked by the muscarinic antagonist atropine, by the M1 antagonist pirenzepine, and by the M3 antagonist 4-DAMP. Pigment granule dispersion was also induced by the M1 agonist 4-[N-(4-chlorophenyl) carbamoyloxy]-4-pent-2-ammonium iodide. In contrast the M2 antagonist AF-DX 116 and the M4 antagonist tropicamide failed to block carbachol-induced dispersion, and the M2 agonist arecaidine but-2-ynyl ester tosylate failed to elicit dispersion. Our results suggest that carbachol-mediated pigment granule dispersion occurs through the activation of Modd muscarinic receptors, which in other systems couple to phosphoinositide hydrolysis and elevation of intracellular calcium. This conclusion must be corroborated by molecular studies, but suggests Ca2+-dependent pathways may be involved in light-adaptive pigment dispersion.

Activation of muscarinic acetylcholine receptors elicits pigment granule dispersion in retinal pigment epithelium isolated from bluegill

Directory of Open Access Journals (Sweden)

Crittenden Elizabeth L

2004-07-01

Full Text Available Abstract Background In fish, melanin pigment granules in the retinal pigment epithelium disperse into apical projections as part of the suite of responses the eye makes to bright light conditions. This pigment granule dispersion serves to reduce photobleaching and occurs in response to neurochemicals secreted by the retina. Previous work has shown that acetylcholine may be involved in inducing light-adaptive pigment dispersion. Acetylcholine receptors are of two main types, nicotinic and muscarinic. Muscarinic receptors are in the G-protein coupled receptor superfamily, and five different muscarinic receptors have been molecularly cloned in human. These receptors are coupled to adenylyl cyclase, calcium mobilization and ion channel activation. To determine the receptor pathway involved in eliciting pigment granule migration, we isolated retinal pigment epithelium from bluegill and subjected it to a battery of cholinergic agents. Results The general cholinergic agonist carbachol induces pigment granule dispersion in isolated retinal pigment epithelium. Carbachol-induced pigment granule dispersion is blocked by the muscarinic antagonist atropine, by the M1 antagonist pirenzepine, and by the M3 antagonist 4-DAMP. Pigment granule dispersion was also induced by the M1 agonist 4-[N-(4-chlorophenyl carbamoyloxy]-4-pent-2-ammonium iodide. In contrast the M2 antagonist AF-DX 116 and the M4 antagonist tropicamide failed to block carbachol-induced dispersion, and the M2 agonist arecaidine but-2-ynyl ester tosylate failed to elicit dispersion. Conclusions Our results suggest that carbachol-mediated pigment granule dispersion occurs through the activation of Modd muscarinic receptors, which in other systems couple to phosphoinositide hydrolysis and elevation of intracellular calcium. This conclusion must be corroborated by molecular studies, but suggests Ca2+-dependent pathways may be involved in light-adaptive pigment dispersion.

Multipurpose Hyperspectral Imaging System

Science.gov (United States)

Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon

2005-01-01

A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral imaging with or without relative movement of the imaging system, and it can be used to scan a target of any size as long as the target can be imaged at the focal plane; for example, automated inspection of food items and identification of single-celled organisms. The spectral resolution of this system is greater than that of prior terrestrial multispectral imaging systems. Moreover, unlike prior high-spectral resolution airborne and spaceborne hyperspectral imaging systems, this system does not rely on relative movement of the target and the imaging system to sweep an imaging line across a scene. This compact system (see figure) consists of a front objective mounted at a translation stage with a motorized actuator, and a line-slit imaging spectrograph mounted within a rotary assembly with a rear adaptor to a charged-coupled-device (CCD) camera. Push-broom scanning is carried out by the motorized actuator which can be controlled either manually by an operator or automatically by a computer to drive the line-slit across an image at a focal plane of the front objective. To reduce the cost, the system has been designed to integrate as many as possible off-the-shelf components including the CCD camera and spectrograph. The system has achieved high spectral and spatial resolutions by using a high-quality CCD camera, spectrograph, and front objective lens. Fixtures for attachment of the system to a microscope (U.S. Patent 6,495,818 B1) make it possible to acquire multispectral images of single cells and other microscopic objects.

Natural pigments and sacred art

Science.gov (United States)

Kelekian, Lena, ,, Lady

2010-05-01

Since the dawn of mankind, cavemen has expressed himself through art. The earliest known cave paintings date to some 32,000 years ago and used 4 colours derived from the earth. These pigments were iron oxides and known as ochres, blacks and whites. All pigments known by the Egyptians, the Greeks, the Romans and Renaissance man were natural and it was not until the 18th century that synthetic pigments were made and widely used. Until that time all art, be it sacred or secular used only natural pigments, of which the preparation of many have been lost or rarely used because of their tedious preparation. As a geologist, a mineralogist and an artist specializing in iconography, I have been able to rediscover 89 natural pigments extracted from minerals. I use these pigments to paint my icons in the traditional Byzantine manner and also to restore old icons, bringing back their glamour and conserving them for years to come. The use of the natural pigments in its proper way also helps to preserve the traditional skills of the iconographer. In the ancient past, pigments were extremely precious. Many took an exceedingly long journey to reach the artists, and came from remote countries. Research into these pigments is the work of history, geography and anthropology. It is an interesting journey in itself to discover that the blue aquamarines came from Afghanistan, the reds from Spain, the greens Africa, and so on. In this contribution I will be describing the origins, preparation and use of some natural pigments, together with their history and provenance. Additionally, I will show how the natural pigments are used in the creation of an icon. Being a geologist iconographer, for me, is a sacrement that transforms that which is earthly, material and natural into a thing of beauty that is sacred. As bread and wine in the Eucharist, water during baptism and oil in Holy Union transmit sanctification to the beholder, natural pigments do the same when one considers an icon. The

Construction of Spectral Discoloration Model for Red Lead Pigment by Aging Test and Simulating Degradation Experiment

Directory of Open Access Journals (Sweden)

Jinxing Liang

2016-01-01

Full Text Available The construction of spectral discoloration model, based on aging test and simulating degradation experiment, was proposed to detect the aging degree of red lead pigment in ancient murals and to reproduce the spectral data supporting digital restoration of the ancient murals. The degradation process of red lead pigment under the aging test conditions was revealed by X-ray diffraction, scanning electron microscopy, and spectrophotometer. The simulating degradation experiment was carried out by proportionally mixing red lead and lead dioxide with referring to the results of aging test. The experimental result indicated that the pure red lead was gradually turned into black lead dioxide, and the amount of tiny particles of the aging sample increased faced with aging process. Both the chroma and lightness of red lead pigment decreased with discoloration, and its hue essentially remains unchanged. In addition, the spectral reflectance curves of the aging samples almost started rising at about 550 nm with the inflection moving slightly from about 570 nm to 550 nm. The spectral reflectance of samples in long- and in short-wavelength regions was fitted well with the logarithmic and linear function. The spectral discoloration model was established, and the real aging red lead pigment in Dunhuang murals was measured and verified the effectiveness of the model.

Phototrophic pigment production with microalgae

NARCIS (Netherlands)

Mulders, K.J.M.

2014-01-01

Abstract

Microalgal pigments are regarded as natural alternatives for food colorants. To facilitate optimization of microalgae-based pigment production, this thesis aimed to obtain key insights in the pigment metabolism of phototrophic microalgae, with the main focus on secondary

High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging.

Science.gov (United States)

Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C

2017-01-01

Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo . These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [ R 2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily ( R 2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy ( R 2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily ( R 2 plant chemical traits. Future

Distributed Unmixing of Hyperspectral Datawith Sparsity Constraint

Science.gov (United States)

Khoshsokhan, S.; Rajabi, R.; Zayyani, H.

2017-09-01

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.

Evaluation of camouflage effectiveness using hyperspectral images

Science.gov (United States)

Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar

2017-10-01

Recent advances in camouflage engineering have made it more difficult to detect targets. Assessing the effectiveness of camouflage against different target detection methods leads to identifying the strengths and weaknesses of camouflage designs. One of the target detection methods is to analyze the content of the scene using remote sensing hyperspectral images. In the process of evaluating camouflage designs, there must be comprehensive and efficient evaluation criteria. Three parameters were considered as the main factors affecting the target detection and based on these factors, camouflage effectiveness assessment criteria were proposed. To combine the criteria in the form of a single equation, the equation used in target visual search models was employed and for determining the criteria, a model was presented based on the structure of the computational visual attention systems. Also, in software implementations on the HyMap hyperspectral image, a variety of camouflage levels were created for the real targets in the image. Assessing the camouflage levels using the proposed criteria, comparing and analyzing the results can show that the provided criteria and model are effective for the evaluation of camouflage designs using hyperspectral images.

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

Directory of Open Access Journals (Sweden)

Marion Jaud

2018-01-01

Full Text Available Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle platforms (<20 kg are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations. This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed domain above small study sites (up to about 4 ha with both high spatial resolution (<10 cm and high spectral resolution (1.85 nm and with georectification accuracy on the order of 1 to 2 m.

Hyperspectral discrimination of camouflaged target

Science.gov (United States)

Bárta, Vojtěch; Racek, František

2017-10-01

The article deals with detection of camouflaged objects during winter season. Winter camouflage is a marginal affair in most countries due to short time period of the snow cover. In the geographical condition of Central Europe the winter period with snow occurs less than 1/12 of year. The LWIR or SWIR spectral areas are used for detection of camouflaged objects. For those spectral regions the difference in chemical composition and temperature express in spectral features. Exploitation of the LWIR and SWIR devices is demanding due to their large dimension and expensiveness. Therefore, the article deals with estimation of utilization of VIS region for detecting of camouflaged object on snow background. The multispectral image output for the various spectral filters is simulated. Hyperspectral indices are determined to detect the camouflaged objects in the winter. The multispectral image simulation is based on the hyperspectral datacube obtained in real conditions.

The Potential of Autonomous Ship-Borne Hyperspectral Radiometers for the Validation of Ocean Color Radiometry Data

Directory of Open Access Journals (Sweden)

Vittorio E. Brando

2016-02-01

Full Text Available Calibration and validation of satellite observations are essential and on-going tasks to ensure compliance with mission accuracy requirements. An automated above water hyperspectral radiometer significantly augmented Australia’s ability to contribute to global and regional ocean color validation and algorithm design activities. The hyperspectral data can be re-sampled for comparison with current and future sensor wavebands. The continuous spectral acquisition along the ship track enables spatial resampling to match satellite footprint. This study reports spectral comparisons of the radiometer data with Visible Infrared Imaging Radiometer Suite (VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua for contrasting water types in tropical waters off northern Australia based on the standard NIR atmospheric correction implemented in SeaDAS. Consistent match-ups are shown for transects of up to 50 km over a range of reflectance values. The MODIS and VIIRS satellite reflectance data consistently underestimated the in situ spectra in the blue with a bias relative to the “dynamic above water radiance and irradiance collector” (DALEC at 443 nm ranging from 9.8 × 10−4 to 3.1 × 10−3 sr−1. Automated acquisition has produced good quality data under standard operating and maintenance procedures. A sensitivity analysis explored the effects of some assumptions in the data reduction methods, indicating the need for a comprehensive investigation and quantification of each source of uncertainty in the estimate of the DALEC reflectances. Deployment on a Research Vessel provides the potential for the radiometric data to be combined with other sampling and observational activities to contribute to algorithm development in the wider bio-optical research community.

THE BENEFITS OF TERRESTRIAL LASER SCANNING AND HYPERSPECTRAL DATA FUSION PRODUCTS

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S. J. Buckley

2012-10-01

Full Text Available Close range hyperspectral imaging is a developing method for the analysis and identification of material composition in many applications, such as in within the earth sciences. Using compact imaging devices in the field allows near-vertical topography to be imaged, thus bypassing the key limitations of viewing angle and resolution that preclude the use of airborne and spaceborne platforms. Terrestrial laser scanning allows 3D topography to be captured with high precision and spatial resolution. The combination of 3D geometry from laser scanning, and material properties from hyperspectral imaging allows new fusion products to be created, adding new information for solving application problems. This paper highlights the advantages of terrestrial lidar and hyperspectral integration, focussing on the qualitative and quantitative aspects, with examples from a geological field application. Accurate co-registration of the two data types is required. This allows 2D pixels to be linked to the 3D lidar geometry, giving increased quantitative analysis as classified material vectors are projected to 3D space for calculation of areas and examination of spatial relationships. User interpretation of hyperspectral results in a spatially-meaningful manner is facilitated using visual methods that combine the geometric and mineralogical products in a 3D environment. Point cloud classification and the use of photorealistic modelling enhance qualitative validation and interpretation, and allow image registration accuracy to be checked. A method for texture mapping of lidar meshes with multiple image textures, both conventional digital photos and hyperspectral results, is described. The integration of terrestrial laser scanning and hyperspectral imaging is a valuable means of providing new analysis methods, suitable for many applications requiring linked geometric and chemical information.

Diversity and functional properties of bistable pigments.

Science.gov (United States)

Tsukamoto, Hisao; Terakita, Akihisa

2010-11-01

Rhodopsin and related opsin-based pigments, which are photosensitive membrane proteins, have been extensively studied using a wide variety of techniques, with rhodopsin being the most understood G protein-coupled receptor (GPCR). Animals use various opsin-based pigments for vision and a wide variety of non-visual functions. Many functionally varied pigments are roughly divided into two kinds, based on their photoreaction: bistable and monostable pigments. Bistable pigments are thermally stable before and after photo-activation, but monostable pigments are stable only before activation. Here, we review the diversity of bistable pigments and their molecular characteristics. We also discuss the mechanisms underlying different molecular characteristics of bistable and monostable pigments. In addition, the potential of bistable pigments as a GPCR model is proposed.

Characterization of Fine Metal Particles Derived from Shredded WEEE Using a Hyperspectral Image System: Preliminary Results

Science.gov (United States)

Candiani, Gabriele; Picone, Nicoletta; Pompilio, Loredana; Pepe, Monica; Colledani, Marcello

2017-01-01

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals’ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%. PMID:28505070

Near-infrared hyperspectral imaging of water evaporation dynamics for early detection of incipient caries.

Science.gov (United States)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2014-10-01

Incipient caries is characterized as demineralization of the tooth enamel reflecting in increased porosity of enamel structure. As a result, the demineralized enamel may contain increased amount of water, and exhibit different water evaporation dynamics than the sound enamel. The objective of this paper is to assess the applicability of water evaporation dynamics of sound and demineralized enamel for detection and quantification of incipient caries using near-infrared hyperspectral imaging. The time lapse of water evaporation from enamel samples with artificial and natural caries lesions of different stages was imaged by a near-infrared hyperspectral imaging system. Partial least squares regression was used to predict the water content from the acquired spectra. The water evaporation dynamics was characterized by a first order logarithmic drying model. The calculated time constants of the logarithmic drying model were used as the discriminative feature. The conducted measurements showed that demineralized enamel contains more water and exhibits significantly faster water evaporation than the sound enamel. By appropriate modelling of the water evaporation process from the enamel surface, the contrast between the sound and demineralized enamel observed in the individual near infrared spectral images can be substantially enhanced. The presented results indicate that near-infrared based prediction of water content combined with an appropriate drying model presents a strong foundation for development of novel diagnostic tools for incipient caries detection. The results of the study enhance the understanding of the water evaporation process from the sound and demineralized enamel and have significant implications for the detection of incipient caries by near-infrared hyperspectral imaging. Copyright © 2014 Elsevier Ltd. All rights reserved.

Skin pigmentation kinetics after UVB exposure

DEFF Research Database (Denmark)

Ravnbak, M.H.; Philipsen, P.A.; Wiegell, S.R.

2008-01-01

There have been few previous studies of the kinetics of pigmentation following ultraviolet B (UVB) exposure, and these have included only fair-skinned persons. The current study investigated pigmentation increase to steady state and fading in 12 Scandinavians and 12 Indians/Pakistanis. Over...... a period of 3 weeks the subjects were UV-irradiated 6 times on the right side of the back and 12 times on the left side using a Solar Simulator and narrowband UVB with equal sub-Minimal Melanogenesis Doses (individually predetermined). Pigmentation was measured from skin remittance at 555 urn and 660 nm...... (allowing correction for erythema). The absolute pigmentation increase was independent of pre-exposure pigmentation, therefore the percentage pigmentation increase was higher in fair-skinned volunteers. The UV dose to minimal pigmentation was higher in darker-skinned persons for single and multiple UV...

Locality-preserving sparse representation-based classification in hyperspectral imagery

Science.gov (United States)

Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting

2016-10-01

This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification.

Spectral reflectance is a reliable water-quality estimator for small, highly turbid wetlands

Czech Academy of Sciences Publication Activity Database

Vinciková, H.; Hanuš, Jan; Pechar, L.

2015-01-01

Roč. 23, č. 5 (2015), s. 933-946 ISSN 0923-4861 R&D Projects: GA MŠk(CZ) LM2010007; GA MŠk 2B06068 Institutional support: RVO:67179843 Keywords : remote sensing * water quality * hyperspectral reflectance * turbid inland waters * chlorophyll * TSS Subject RIV: DA - Hydrology ; Limnology Impact factor: 1.407, year: 2015

Colorectal cancer detection by hyperspectral imaging using fluorescence excitation scanning

Science.gov (United States)

Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.

2018-02-01

Hyperspectral imaging technologies have shown great promise for biomedical applications. These techniques have been especially useful for detection of molecular events and characterization of cell, tissue, and biomaterial composition. Unfortunately, hyperspectral imaging technologies have been slow to translate to clinical devices - likely due to increased cost and complexity of the technology as well as long acquisition times often required to sample a spectral image. We have demonstrated that hyperspectral imaging approaches which scan the fluorescence excitation spectrum can provide increased signal strength and faster imaging, compared to traditional emission-scanning approaches. We have also demonstrated that excitation-scanning approaches may be able to detect spectral differences between colonic adenomas and adenocarcinomas and normal mucosa in flash-frozen tissues. Here, we report feasibility results from using excitation-scanning hyperspectral imaging to screen pairs of fresh tumoral and nontumoral colorectal tissues. Tissues were imaged using a novel hyperspectral imaging fluorescence excitation scanning microscope, sampling a wavelength range of 360-550 nm, at 5 nm increments. Image data were corrected to achieve a NIST-traceable flat spectral response. Image data were then analyzed using a range of supervised and unsupervised classification approaches within ENVI software (Harris Geospatial Solutions). Supervised classification resulted in >99% accuracy for single-patient image data, but only 64% accuracy for multi-patient classification (n=9 to date), with the drop in accuracy due to increased false-positive detection rates. Hence, initial data indicate that this approach may be a viable detection approach, but that larger patient sample sizes need to be evaluated and the effects of inter-patient variability studied.

Hyperspectral Image Analysis of Food Quality

DEFF Research Database (Denmark)

Arngren, Morten

inspection.Near-infrared spectroscopy can address these issues by offering a fast and objectiveanalysis of the food quality. A natural extension to these single spectrumNIR systems is to include image information such that each pixel holds a NIRspectrum. This augmented image information offers several......Assessing the quality of food is a vital step in any food processing line to ensurethe best food quality and maximum profit for the farmer and food manufacturer.Traditional quality evaluation methods are often destructive and labourintensive procedures relying on wet chemistry or subjective human...... extensions to the analysis offood quality. This dissertation is concerned with hyperspectral image analysisused to assess the quality of single grain kernels. The focus is to highlight thebenefits and challenges of using hyperspectral imaging for food quality presentedin two research directions. Initially...

Manufacturing and coating of optical components for the EnMAP hyperspectral imager

Science.gov (United States)

Schürmann, M.; Gäbler, D.; Schlegel, R.; Schwinde, S.; Peschel, T.; Damm, C.; Jende, R.; Kinast, J.; Müller, S.; Beier, M.; Risse, S.; Sang, B.; Glier, M.; Bittner, H.; Erhard, M.

2016-07-01

The optical system of the hyperspectral imager of the Environmental Mapping and Analysis Program (EnMAP) consists of a three-mirror anastigmat (TMA) and two independent spectrometers working in the VNIR and SWIR spectral range, respectively. The VNIR spectrometer includes a spherical NiP coated Al6061 mirror that has been ultra-precisely diamond turned and finally coated with protected silver as well as four curved fused silica (FS) and flint glass (SF6) prisms, respectively, each with broadband antireflection (AR) coating, while the backs of the two outer prisms are coated with a high-reflective coating. For AR coating, plasma ion assisted deposition (PIAD) has been used; the high-reflective enhanced Ag-coating on the backside has been deposited by magnetron sputtering. The SWIR spectrometer contains four plane and spherical gold-coated mirrors, respectively, and two curved FS prisms with a broadband antireflection coating. Details about the ultra-precise manufacturing of metal mirrors and prisms as well as their coating are presented in this work.

The challenges of analysing blood stains with hyperspectral imaging

Science.gov (United States)

Kuula, J.; Puupponen, H.-H.; Rinta, H.; Pölönen, I.

2014-06-01

Hyperspectral imaging is a potential noninvasive technology for detecting, separating and identifying various substances. In the forensic and military medicine and other CBRNE related use it could be a potential method for analyzing blood and for scanning other human based fluids. For example, it would be valuable to easily detect whether some traces of blood are from one or more persons or if there are some irrelevant substances or anomalies in the blood. This article represents an experiment of separating four persons' blood stains on a white cotton fabric with a SWIR hyperspectral camera and FT-NIR spectrometer. Each tested sample includes standardized 75 _l of 100 % blood. The results suggest that on the basis of the amount of erythrocytes in the blood, different people's blood might be separable by hyperspectral analysis. And, referring to the indication given by erythrocytes, there might be a possibility to find some other traces in the blood as well. However, these assumptions need to be verified with wider tests, as the number of samples in the study was small. According to the study there also seems to be several biological, chemical and physical factors which affect alone and together on the hyperspectral analyzing results of blood on fabric textures, and these factors need to be considered before making any further conclusions on the analysis of blood on various materials.

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-NIR hyperspectral imaging.

Science.gov (United States)

Moscetti, Roberto; Sturm, Barbara; Crichton, Stuart Oj; Amjad, Waseem; Massantini, Riccardo

2018-05-01

The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ 1 ]-R[λ 2 ] and R[λ 1 ]:R[λ 2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

METHODOLOGY FOR DETERMINING OPTIMAL EXPOSURE PARAMETERS OF A HYPERSPECTRAL SCANNING SENSOR

Directory of Open Access Journals (Sweden)

P. Walczykowski

2016-06-01

Full Text Available The purpose of the presented research was to establish a methodology that would allow the registration of hyperspectral images with a defined spatial resolution on a horizontal plane. The results obtained within this research could then be used to establish the optimum sensor and flight parameters for collecting aerial imagery data using an UAV or other aerial system. The methodology is based on an user-selected optimal camera exposure parameters (i.e. time, gain value and flight parameters (i.e. altitude, velocity. A push-broom hyperspectral imager- the Headwall MicroHyperspec A-series VNIR was used to conduct this research. The measurement station consisted of the following equipment: a hyperspectral camera MicroHyperspec A-series VNIR, a personal computer with HyperSpec III software, a slider system which guaranteed the stable motion of the sensor system, a white reference panel and a Siemens star, which was used to evaluate the spatial resolution. Hyperspectral images were recorded at different distances between the sensor and the target- from 5m to 100m. During the registration process of each acquired image, many exposure parameters were changed, such as: the aperture value, exposure time and speed of the camera’s movement on the slider. Based on all of the registered hyperspectral images, some dependencies between chosen parameters had been developed: - the Ground Sampling Distance – GSD and the distance between the sensor and the target, - the speed of the camera and the distance between the sensor and the target, - the exposure time and the gain value, - the Density Number and the gain value. The developed methodology allowed us to determine the speed and the altitude of an unmanned aerial vehicle on which the sensor would be mounted, ensuring that the registered hyperspectral images have the required spatial resolution.

Parallel computation for blood cell classification in medical hyperspectral imagery

International Nuclear Information System (INIS)

Li, Wei; Wu, Lucheng; Qiu, Xianbo; Ran, Qiong; Xie, Xiaoming

2016-01-01

With the advantage of fine spectral resolution, hyperspectral imagery provides great potential for cell classification. This paper provides a promising classification system including the following three stages: (1) band selection for a subset of spectral bands with distinctive and informative features, (2) spectral-spatial feature extraction, such as local binary patterns (LBP), and (3) followed by an effective classifier. Moreover, these three steps are further implemented on graphics processing units (GPU) respectively, which makes the system real-time and more practical. The GPU parallel implementation is compared with the serial implementation on central processing units (CPU). Experimental results based on real medical hyperspectral data demonstrate that the proposed system is able to offer high accuracy and fast speed, which are appealing for cell classification in medical hyperspectral imagery. (paper)

Ore minerals textural characterization by hyperspectral imaging

Science.gov (United States)

Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia

2013-02-01

The utilization of hyperspectral detection devices, for natural resources mapping/exploitation through remote sensing techniques, dates back to the early 1970s. From the first devices utilizing a one-dimensional profile spectrometer, HyperSpectral Imaging (HSI) devices have been developed. Thus, from specific-customized devices, originally developed by Governmental Agencies (e.g. NASA, specialized research labs, etc.), a lot of HSI based equipment are today available at commercial level. Parallel to this huge increase of hyperspectral systems development/manufacturing, addressed to airborne application, a strong increase also occurred in developing HSI based devices for "ground" utilization that is sensing units able to play inside a laboratory, a processing plant and/or in an open field. Thanks to this diffusion more and more applications have been developed and tested in this last years also in the materials sectors. Such an approach, when successful, is quite challenging being usually reliable, robust and characterised by lower costs if compared with those usually associated to commonly applied analytical off- and/or on-line analytical approaches. In this paper such an approach is presented with reference to ore minerals characterization. According to the different phases and stages of ore minerals and products characterization, and starting from the analyses of the detected hyperspectral firms, it is possible to derive useful information about mineral flow stream properties and their physical-chemical attributes. This last aspect can be utilized to define innovative process mineralogy strategies and to implement on-line procedures at processing level. The present study discusses the effects related to the adoption of different hardware configurations, the utilization of different logics to perform the analysis and the selection of different algorithms according to the different characterization, inspection and quality control actions to apply.

Hyperspectral Soil Mapper (HYSOMA) software interface: Review and future plans

Science.gov (United States)

Chabrillat, Sabine; Guillaso, Stephane; Eisele, Andreas; Rogass, Christian

2014-05-01

With the upcoming launch of the next generation of hyperspectral satellites that will routinely deliver high spectral resolution images for the entire globe (e.g. EnMAP, HISUI, HyspIRI, HypXIM, PRISMA), an increasing demand for the availability/accessibility of hyperspectral soil products is coming from the geoscience community. Indeed, many robust methods for the prediction of soil properties based on imaging spectroscopy already exist and have been successfully used for a wide range of soil mapping airborne applications. Nevertheless, these methods require expert know-how and fine-tuning, which makes them used sparingly. More developments are needed toward easy-to-access soil toolboxes as a major step toward the operational use of hyperspectral soil products for Earth's surface processes monitoring and modelling, to allow non-experienced users to obtain new information based on non-expensive software packages where repeatability of the results is an important prerequisite. In this frame, based on the EU-FP7 EUFAR (European Facility for Airborne Research) project and EnMAP satellite science program, higher performing soil algorithms were developed at the GFZ German Research Center for Geosciences as demonstrators for end-to-end processing chains with harmonized quality measures. The algorithms were built-in into the HYSOMA (Hyperspectral SOil MApper) software interface, providing an experimental platform for soil mapping applications of hyperspectral imagery that gives the choice of multiple algorithms for each soil parameter. The software interface focuses on fully automatic generation of semi-quantitative soil maps such as soil moisture, soil organic matter, iron oxide, clay content, and carbonate content. Additionally, a field calibration option calculates fully quantitative soil maps provided ground truth soil data are available. Implemented soil algorithms have been tested and validated using extensive in-situ ground truth data sets. The source of the HYSOMA

Hyperspectral Sensing for Turbid Water Quality Monitoring in Freshwater Rivers: Empirical Relationship between Reflectance and Turbidity and Total Solids

Directory of Open Access Journals (Sweden)

Jiunn-Lin Wu

2014-11-01

Full Text Available Total suspended solid (TSS is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR and artificial neural network (ANN were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L−1 and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L−1 and less than 600 NTU, respectively and used rather than using whole dataset (R2 = 0.93 versus 0.88 for turbidity and R2 = 0.83 versus 0.58 for TSS. On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R2 = 0.66 was better than with the MR

Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids.

Science.gov (United States)

Wu, Jiunn-Lin; Ho, Chung-Ru; Huang, Chia-Ching; Srivastav, Arun Lal; Tzeng, Jing-Hua; Lin, Yao-Tung

2014-11-28

Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L(-1) and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L(-1) and less than 600 NTU, respectively and used rather than using whole dataset (R(2) = 0.93 versus 0.88 for turbidity and R(2) = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R(2) = 0.66) was better than with the MR approach (R

Production of Monascus-like pigments

DEFF Research Database (Denmark)

2012-01-01

the cultivation medium with an inoculum of Penicillium to form a cultivation composition; d) cultivating the inoculated cultivation composition of (c); e) separating the one or more produced pigment compositions. The method of the invention may be used for producing Monascus-like pigment compositions for use......The present invention relates to a method for producing one or more Monascus-like pigment composition from Penicillium species comprising: a) providing a cultivation medium comprising a high concentration of C-and N-sources and a high C/N molar ratio, b) adjusting pH to about 5 to 8, c) inoculating...... as colouring agents in food items or non food items. The inventions further relates to Monascus-like pigment composition obtainable by a method of the inventions as well as use of the pigments....

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

Science.gov (United States)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Characterisation and geostatistical analysis of clay rocks in underground facilities using hyper-spectral images

International Nuclear Information System (INIS)

Becker, J.K.; Marschall, P.; Brunner, P.; Cholet, C.; Renard, P.; Buckley, S.; Kurz, T.

2012-01-01

, and are readily available as spectral libraries for use in software processing packages. Since rocks are composites of minerals, their spectra represent a mixture of spectra of the constituent minerals concerning the reflectance. In general, imaging spectrometry allows a semi-quantitative analysis of mineral abundances from rock spectra, for example by analysing the intensity of absorption bands. In many cases a mineral with a unique absorption signature can be correlated to a specific lithological unit, which can be used to trace and map the lithology. Additionally, abundance and spatial variation can be determined from the rock spectra. Common reflection features in sedimentary rocks are typically related to carbonate and clay minerals, hydroxyl, water or iron-bearing material and weathering products. A number of physical properties can influence the intensity of features in the spectral curves of minerals and rocks, such as particle size, angle of incidence, porosity and surface roughness, though the wavelength positions of the absorption features are not changed. Next to the obvious ability to use the hyper-spectral images to 'visually' correlate layers within a rock over a certain distance they can also be used for a more rigorous approach of geostatistical correlation. We have developed a work flow for this approach using the hyper-spectral image classifications: 1. In a first step, image reconstruction must be performed. During the scanning and possibly also later during classification, some areas of the hyper-spectral images may not be completely usable or some pixels may not have been classified. In this case, the 'holes' should be filled using multiple-point geostatistical techniques. 2. In the present example, images at three different resolutions have been taken. It is envisaged to use the high resolution images and simulate the high resolution over the entire rock face in a way that the high resolution simulations are guided by the low resolution images

UV photoreceptors and UV-yellow wing pigments in Heliconius butterflies allow a color signal to serve both mimicry and intraspecific communication.

Science.gov (United States)

Bybee, Seth M; Yuan, Furong; Ramstetter, Monica D; Llorente-Bousquets, Jorge; Reed, Robert D; Osorio, Daniel; Briscoe, Adriana D

2012-01-01

Mimetic wing coloration evolves in butterflies in the context of predator confusion. Unless butterfly eyes have adaptations for discriminating mimetic color variation, mimicry also carries a risk of confusion for the butterflies themselves. Heliconius butterfly eyes, which express recently duplicated ultraviolet (UV) opsins, have such an adaptation. To examine bird and butterfly color vision as sources of selection on butterfly coloration, we studied yellow wing pigmentation in the tribe Heliconiini. We confirmed, using reflectance and mass spectrometry, that only Heliconius use 3-hydroxy-DL-kynurenine (3-OHK), which looks yellow to humans but reflects both UV- and long-wavelength light, whereas butterflies in related genera have chemically unknown yellow pigments mostly lacking UV reflectance. Modeling of these color signals reveals that the two UV photoreceptors of Heliconius are better suited to separating 3-OHK from non-3-OHK spectra compared with the photoreceptors of related genera or birds. The co-occurrence of potentially enhanced UV vision and a UV-reflecting yellow wing pigment could allow unpalatable Heliconius private intraspecific communication in the presence of mimics. Our results are the best available evidence for the correlated evolution of a color signal and color vision. They also suggest that predator visual systems are error prone in the context of mimicry. © 2011 by The University of Chicago.

Manifold structure preservative for hyperspectral target detection

Science.gov (United States)

Imani, Maryam

2018-05-01

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

Activation of muscarinic acetylcholine receptors elicits pigment granule dispersion in retinal pigment epithelium isolated from bluegill

OpenAIRE

González, Alfredo; Crittenden, Elizabeth L; García, Dana M

2004-01-01

Abstract Background In fish, melanin pigment granules in the retinal pigment epithelium disperse into apical projections as part of the suite of responses the eye makes to bright light conditions. This pigment granule dispersion serves to reduce photobleaching and occurs in response to neurochemicals secreted by the retina. Previous work has shown that acetylcholine may be involved in inducing light-adaptive pigment dispersion. Acetylcholine receptors are of two main types, nicotinic and musc...

Wavelength dependence of the bidirectional reflectance distribution function (BRDF) of beach sands.

Science.gov (United States)

Doctor, Katarina Z; Bachmann, Charles M; Gray, Deric J; Montes, Marcos J; Fusina, Robert A

2015-11-01

The wavelength dependence of the dominant directional reflective properties of beach sands was demonstrated using principal component analysis and the related correlation matrix. In general, we found that the hyperspectral bidirectional reflectance distribution function (BRDF) of beach sands has weak wavelength dependence. Its BRDF varies slightly in three broad wavelength regions. The variations are more evident in surfaces of greater visual roughness than in smooth surfaces. The weak wavelength dependence of the BRDF of beach sand can be captured using three broad wavelength regions instead of hundreds of individual wavelengths.

Pigment production from a mangrove Penicillium

African Journals Online (AJOL)

SAM

2014-06-25

Jun 25, 2014 ... Key words: Penicillium, 2-(4-acetyl phenyl) acetic acid, bio elements, salts, soluble pigment. .... Table 1. Characteristics of the pigment fractions after solvent extraction. ..... naphthoquinone pigment by Fusarium verticillioides.

Characterization of Maritime Pine Forests with Combination of Simulated P-Band SAR Data and Hyperspectral Data

Directory of Open Access Journals (Sweden)

C. Albinet

2012-01-01

Full Text Available This paper describes a sensitivity study performed on simulated radar and optical remote sensing forest data. It presents how the dual model has been built up. The first step is a forest growth model fed with biophysical parameters. The geometrical description is then the input of an optical hyperspectral model, giving reflectance spectra, and a Synthetic Aperture Radar (SAR model, giving the polarimetric and interferometric observables. As an illustration, the first results obtained by both models outputs are presented, and fusions of these outputs are performed.

The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image

CSIR Research Space (South Africa)

Cawse K

2011-06-01

Full Text Available WHITENING ON METHODS FOR DETERMINING THE INTRINSIC DIMENSION OF A HYPERSPECTRAL IMAGE K. Cawse1;2, A. Robin1, M. Sears3 1School of Computational and Applied Maths 2Remote Sensing Research Unit 3School of Computer Science Meraka Institute, CSIR... the reflectance measured in each pixel i as a vector xi = [xi1; : : : ; xip]T for 1 i N . This research is part of the Centre for High Performance Computing flagship project: Computational Research Initiative in Imaging and Remote Sensing. M. Sears and A...

SIBI: A compact hyperspectral camera in the mid-infrared

Science.gov (United States)

Pola Fossi, Armande; Ferrec, Yann; Domel, Roland; Coudrain, Christophe; Guerineau, Nicolas; Roux, Nicolas; D'Almeida, Oscar; Bousquet, Marc; Kling, Emmanuel; Sauer, Hervé

2015-10-01

Recent developments in unmanned aerial vehicles have increased the demand for more and more compact optical systems. In order to bring solutions to this demand, several infrared systems are being developed at ONERA such as spectrometers, imaging devices, multispectral and hyperspectral imaging systems. In the field of compact infrared hyperspectral imaging devices, ONERA and Sagem Défense et Sécurité have collaborated to develop a prototype called SIBI, which stands for "Spectro-Imageur Birefringent Infrarouge". It is a static Fourier transform imaging spectrometer which operates in the mid-wavelength infrared spectral range and uses a birefringent lateral shearing interferometer. Up to now, birefringent interferometers have not been often used for hyperspectral imaging in the mid-infrared because of the lack of crystal manufacturers, contrary to the visible spectral domain where the production of uniaxial crystals like calcite are mastered for various optical applications. In the following, we will present the design and the realization of SIBI as well as the first experimental results.

A Novel Measurement Matrix Optimization Approach for Hyperspectral Unmixing

Directory of Open Access Journals (Sweden)

Su Xu

2017-01-01

Full Text Available Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy. This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS theory for hyperspectral unmixing. Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence. Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

Science.gov (United States)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Spatial and temporal variability of hyperspectral signatures of terrain

Science.gov (United States)

Jones, K. F.; Perovich, D. K.; Koenig, G. G.

2008-04-01

Electromagnetic signatures of terrain exhibit significant spatial heterogeneity on a range of scales as well as considerable temporal variability. A statistical characterization of the spatial heterogeneity and spatial scaling algorithms of terrain electromagnetic signatures are required to extrapolate measurements to larger scales. Basic terrain elements including bare soil, grass, deciduous, and coniferous trees were studied in a quasi-laboratory setting using instrumented test sites in Hanover, NH and Yuma, AZ. Observations were made using a visible and near infrared spectroradiometer (350 - 2500 nm) and hyperspectral camera (400 - 1100 nm). Results are reported illustrating: i) several difference scenes; ii) a terrain scene time series sampled over an annual cycle; and iii) the detection of artifacts in scenes. A principal component analysis indicated that the first three principal components typically explained between 90 and 99% of the variance of the 30 to 40-channel hyperspectral images. Higher order principal components of hyperspectral images are useful for detecting artifacts in scenes.

The cuticle modulates ultraviolet reflectance of avian eggshells

Directory of Open Access Journals (Sweden)

Daphne C. Fecheyr-Lippens

2015-07-01

Full Text Available Avian eggshells are variedly coloured, yet only two pigments, biliverdin and protoporphyrin IX, are known to contribute to the dramatic diversity of their colours. By contrast, the contributions of structural or other chemical components of the eggshell are poorly understood. For example, unpigmented eggshells, which appear white to the human eye, vary in their ultraviolet (UV reflectance, which may be detectable by birds. We investigated the proximate mechanisms for the variation in UV-reflectance of unpigmented bird eggshells using spectrophotometry, electron microscopy, chemical analyses, and experimental manipulations. We specifically tested how UV-reflectance is affected by the eggshell cuticle, the outermost layer of most avian eggshells. The chemical dissolution of the outer eggshell layers, including the cuticle, increased UV-reflectance for only eggshells that contained a cuticle. Our findings demonstrate that the outer eggshell layers, including the cuticle, absorb UV-light, probably because they contain higher levels of organic components and other chemicals, such as calcium phosphates, compared to the predominantly calcite-based eggshell matrix. These data highlight the need to examine factors other than the known pigments in studies of avian eggshell colour.

[Study on the modeling of earth-atmosphere coupling over rugged scenes for hyperspectral remote sensing].

Science.gov (United States)

Zhao, Hui-Jie; Jiang, Cheng; Jia, Guo-Rui

2014-01-01

Adjacency effects may introduce errors in the quantitative applications of hyperspectral remote sensing, of which the significant item is the earth-atmosphere coupling radiance. However, the surrounding relief and shadow induce strong changes in hyperspectral images acquired from rugged terrain, which is not accurate to describe the spectral characteristics. Furthermore, the radiative coupling process between the earth and the atmosphere is more complex over the rugged scenes. In order to meet the requirements of real-time processing in data simulation, an equivalent reflectance of background was developed by taking into account the topography and the geometry between surroundings and targets based on the radiative transfer process. The contributions of the coupling to the signal at sensor level were then evaluated. This approach was integrated to the sensor-level radiance simulation model and then validated through simulating a set of actual radiance data. The results show that the visual effect of simulated images is consistent with that of observed images. It was also shown that the spectral similarity is improved over rugged scenes. In addition, the model precision is maintained at the same level over flat scenes.

Near-infrared hyperspectral imaging for quality analysis of agricultural and food products

Science.gov (United States)

Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G.

2010-04-01

Agricultural and food processing industries are always looking to implement real-time quality monitoring techniques as a part of good manufacturing practices (GMPs) to ensure high-quality and safety of their products. Near-infrared (NIR) hyperspectral imaging is gaining popularity as a powerful non-destructive tool for quality analysis of several agricultural and food products. This technique has the ability to analyse spectral data in a spatially resolved manner (i.e., each pixel in the image has its own spectrum) by applying both conventional image processing and chemometric tools used in spectral analyses. Hyperspectral imaging technique has demonstrated potential in detecting defects and contaminants in meats, fruits, cereals, and processed food products. This paper discusses the methodology of hyperspectral imaging in terms of hardware, software, calibration, data acquisition and compression, and development of prediction and classification algorithms and it presents a thorough review of the current applications of hyperspectral imaging in the analyses of agricultural and food products.

Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes

Science.gov (United States)

Honkavaara, Eija; Rosnell, Tomi; Oliveira, Raquel; Tommaselli, Antonio

2017-12-01

A recent revolution in miniaturised sensor technology has provided markets with novel hyperspectral imagers operating in the frame format principle. In the case of unmanned aerial vehicle (UAV) based remote sensing, the frame format technology is highly attractive in comparison to the commonly utilised pushbroom scanning technology, because it offers better stability and the possibility to capture stereoscopic data sets, bringing an opportunity for 3D hyperspectral object reconstruction. Tuneable filters are one of the approaches for capturing multi- or hyperspectral frame images. The individual bands are not aligned when operating a sensor based on tuneable filters from a mobile platform, such as UAV, because the full spectrum recording is carried out in the time-sequential principle. The objective of this investigation was to study the aspects of band registration of an imager based on tuneable filters and to develop a rigorous and efficient approach for band registration in complex 3D scenes, such as forests. The method first determines the orientations of selected reference bands and reconstructs the 3D scene using structure-from-motion and dense image matching technologies. The bands, without orientation, are then matched to the oriented bands accounting the 3D scene to provide exterior orientations, and afterwards, hyperspectral orthomosaics, or hyperspectral point clouds, are calculated. The uncertainty aspects of the novel approach were studied. An empirical assessment was carried out in a forested environment using hyperspectral images captured with a hyperspectral 2D frame format camera, based on a tuneable Fabry-Pérot interferometer (FPI) on board a multicopter and supported by a high spatial resolution consumer colour camera. A theoretical assessment showed that the method was capable of providing band registration accuracy better than 0.5-pixel size. The empirical assessment proved the performance and showed that, with the novel method, most parts of

Target Detection Using an AOTF Hyperspectral Imager

Science.gov (United States)

Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.

1994-01-01

This paper reports results of a recent field experiment using a prototype system to evaluate the acousto-optic tunable filter polarimetric hyperspectral imaging technology for target detection applications.

Study on Hyperspectral Characteristics and Estimation Model of Soil Mercury Content

Science.gov (United States)

Liu, Jinbao; Dong, Zhenyu; Sun, Zenghui; Ma, Hongchao; Shi, Lei

2017-12-01

In this study, the mercury content of 44 soil samples in Guan Zhong area of Shaanxi Province was used as the data source, and the reflectance spectrum of soil was obtained by ASD Field Spec HR (350-2500 nm) Comparing the reflection characteristics of different contents and the effect of different pre-treatment methods on the establishment of soil heavy metal spectral inversion model. The first order differential, second order differential and reflectance logarithmic transformations were carried out after the pre-treatment of NOR, MSC and SNV, and the sensitive bands of reflectance and mercury content in different mathematical transformations were selected. A hyperspectral estimation model is established by regression method. The results of chemical analysis show that there is a serious Hg pollution in the study area. The results show that: (1) the reflectivity decreases with the increase of mercury content, and the sensitive regions of mercury are located at 392 ~ 455nm, 923nm ~ 1040nm and 1806nm ~ 1969nm. (2) The combination of NOR, MSC and SNV transformations combined with differential transformations can improve the information of heavy metal elements in the soil, and the combination of high correlation band can improve the stability and prediction ability of the model. (3) The partial least squares regression model based on the logarithm of the original reflectance is better and the precision is higher, Rc2 = 0.9912, RMSEC = 0.665; Rv2 = 0.9506, RMSEP = 1.93, which can achieve the mercury content in this region Quick forecast.

On-line database of voltammetric data of immobilized particles for identifying pigments and minerals in archaeometry, conservation and restoration (ELCHER database)

Energy Technology Data Exchange (ETDEWEB)

Doménech-Carbó, Antonio, E-mail: antonio.domenech@uv.es [Departament de Química Analítica, Universitat de València, Dr. Moliner, 50, 46100, Burjassot, València (Spain); Doménech-Carbó, María Teresa, E-mail: tdomenec@crbc.upv.es [Institut de Restauració del Patrimoni, Universitat Politècnica de València, Camí de Vera 14, 46022, València (Spain); Valle-Algarra, Francisco Manuel; Gimeno-Adelantado, José Vicente [Departament de Química Analítica, Universitat de València, Dr. Moliner, 50, 46100, Burjassot, València (Spain); Osete-Cortina, Laura [Institut de Restauració del Patrimoni, Universitat Politècnica de València, Camí de Vera 14, 46022, València (Spain); Bosch-Reig, Francisco [Departament de Química Analítica, Universitat de València, Dr. Moliner, 50, 46100, Burjassot, València (Spain)

2016-07-13

A web-based database of voltammograms is presented for characterizing artists' pigments and corrosion products of ceramic, stone and metal objects by means of the voltammetry of immobilized particles methodology. Description of the website and the database is provided. Voltammograms are, in most cases, accompanied by scanning electron microphotographs, X-ray spectra, infrared spectra acquired in attenuated total reflectance Fourier transform infrared spectroscopy mode (ATR-FTIR) and diffuse reflectance spectra in the UV–Vis-region. For illustrating the usefulness of the database two case studies involving identification of pigments and a case study describing deterioration of an archaeological metallic object are presented. - Highlights: • A web-based database of voltammograms is presented. • The voltammetry of immobilized particles is used. • Artist's pigments and corrosion products of ceramic, stone and metal objects are included. • Examples of application on works of art are discussed.

On-line database of voltammetric data of immobilized particles for identifying pigments and minerals in archaeometry, conservation and restoration (ELCHER database)

International Nuclear Information System (INIS)

Doménech-Carbó, Antonio; Doménech-Carbó, María Teresa; Valle-Algarra, Francisco Manuel; Gimeno-Adelantado, José Vicente; Osete-Cortina, Laura; Bosch-Reig, Francisco

2016-01-01

A web-based database of voltammograms is presented for characterizing artists' pigments and corrosion products of ceramic, stone and metal objects by means of the voltammetry of immobilized particles methodology. Description of the website and the database is provided. Voltammograms are, in most cases, accompanied by scanning electron microphotographs, X-ray spectra, infrared spectra acquired in attenuated total reflectance Fourier transform infrared spectroscopy mode (ATR-FTIR) and diffuse reflectance spectra in the UV–Vis-region. For illustrating the usefulness of the database two case studies involving identification of pigments and a case study describing deterioration of an archaeological metallic object are presented. - Highlights: • A web-based database of voltammograms is presented. • The voltammetry of immobilized particles is used. • Artist's pigments and corrosion products of ceramic, stone and metal objects are included. • Examples of application on works of art are discussed.

A Spectral-Texture Kernel-Based Classification Method for Hyperspectral Images

Directory of Open Access Journals (Sweden)

Yi Wang

2016-11-01

Full Text Available Classification of hyperspectral images always suffers from high dimensionality and very limited labeled samples. Recently, the spectral-spatial classification has attracted considerable attention and can achieve higher classification accuracy and smoother classification maps. In this paper, a novel spectral-spatial classification method for hyperspectral images by using kernel methods is investigated. For a given hyperspectral image, the principle component analysis (PCA transform is first performed. Then, the first principle component of the input image is segmented into non-overlapping homogeneous regions by using the entropy rate superpixel (ERS algorithm. Next, the local spectral histogram model is applied to each homogeneous region to obtain the corresponding texture features. Because this step is performed within each homogenous region, instead of within a fixed-size image window, the obtained local texture features in the image are more accurate, which can effectively benefit the improvement of classification accuracy. In the following step, a contextual spectral-texture kernel is constructed by combining spectral information in the image and the extracted texture information using the linearity property of the kernel methods. Finally, the classification map is achieved by the support vector machines (SVM classifier using the proposed spectral-texture kernel. Experiments on two benchmark airborne hyperspectral datasets demonstrate that our method can effectively improve classification accuracies, even though only a very limited training sample is available. Specifically, our method can achieve from 8.26% to 15.1% higher in terms of overall accuracy than the traditional SVM classifier. The performance of our method was further compared to several state-of-the-art classification methods of hyperspectral images using objective quantitative measures and a visual qualitative evaluation.

Classification of High Spatial Resolution, Hyperspectral ...

Science.gov (United States)

EPA announced the availability of the final report,Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA . This report and associated land use/land cover (LULC) coverage is the result of a collaborative effort among an interdisciplinary team of scientists with the U.S. Environmental Protection Agency's (U.S. EPA's) Office of Research and Development in Cincinnati, Ohio. A primary goal of this project is to enhance the use of geography and spatial analytic tools in risk assessment, and to improve the scientific basis for risk management decisions affecting drinking water and water quality. The land use/land cover classification is derived from 82 flight lines of Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery acquired from July 24 through August 9, 2002 via fixed-wing aircraft.

Planetary Hyperspectral Imager (PHI)

Science.gov (United States)

Silvergate, Peter

1996-01-01

A hyperspectral imaging spectrometer was breadboarded. Key innovations were use of a sapphire prism and single InSb focal plane to cover the entire spectral range, and a novel slit optic and relay optics to reduce thermal background. Operation over a spectral range of 450 - 4950 nm (approximately 3.5 spectral octaves) was demonstrated. Thermal background reduction by a factor of 8 - 10 was also demonstrated.

Hyper-spectral frequency selection for the classification of vegetation diseases

OpenAIRE

Dijkstra, Klaas; van de Loosdrecht, Jaap; Schomaker, Lambert; Wiering, Marco

2017-01-01

Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the...

Hairless pigmented guinea pigs: a new model for the study of mammalian pigmentation.

Science.gov (United States)

Bolognia, J L; Murray, M S; Pawelek, J M

1990-09-01

A stock of hairless pigmented guinea pigs was developed to facilitate studies of mammalian pigmentation. This stock combines the convenience of a hairless animal with a pigmentary system that is similar to human skin. In both human and guinea pig skin, active melanocytes are located in the basal layer of the interfollicular epidermis. Hairless albino guinea pigs on an outbred Hartley background (CrI:IAF/HA(hr/hr)BR; designated hr/hr) were mated with red-haired guinea pigs (designated Hr/Hr). Red-haired heterozygotes from the F1 generation (Hr/hr) were then mated with each other or with hairless albino guinea pigs. The F2 generation included hairless pigmented guinea pigs that retained their interfollicular epidermal melanocytes and whose skin was red-brown in color. Following UV irradiation, there was an increase in cutaneous pigmentation as well as an increase in the number of active epidermal melanocytes. An additional strain of black hairless guinea pigs was developed using black Hr/Hr animals and a similar breeding scheme. These two strains should serve as useful models for studies of the mammalian pigment system.

Variables separation of the spectral BRDF for better understanding color variation in special effect pigment coatings.

Science.gov (United States)

Ferrero, Alejandro; Rabal, Ana María; Campos, Joaquín; Pons, Alicia; Hernanz, María Luisa

2012-06-01

A type of representation of the spectral bidirectional reflectance distribution function (BRDF) is proposed that distinctly separates the spectral variable (wavelength) from the geometrical variables (spherical coordinates of the irradiation and viewing directions). Principal components analysis (PCA) is used in order to decompose the spectral BRDF in decorrelated spectral components, and the weight that they have at every geometrical configuration of irradiation/viewing is established. This method was applied to the spectral BRDF measurement of a special effect pigment sample, and four principal components with relevant variance were identified. These four components are enough to reproduce the great diversity of spectral reflectances observed at different geometrical configurations. Since this representation is able to separate spectral and geometrical variables, it facilitates the interpretation of the color variation of special effect pigments coatings versus the geometrical configuration of irradiation/viewing.

Classification of lead white pigments using synchrotron radiation micro X-ray diffraction

International Nuclear Information System (INIS)

Welcomme, E.; Walter, P.; Menu, M.; Bleuet, P.; Hodeau, J.L.; Dooryhee, E.; Martinetto, P.

2007-01-01

Lead white pigment was used and synthesised for cosmetic and artistic purposes since the antiquity. Ancient texts describe the various recipes, and preparation processes as well as locations of production. In this study, we describe the results achieved on several paint samples taken from Matthias Gruenewald's works. Gruenewald, who was active between 1503 and 1524, was a major painter at the beginning of the German Renaissance. Thanks to X-ray diffraction analysis using synchrotron radiation, it is possible to associate the composition of the paint samples with the masters ancient recipes. Different approaches were used, in reflection and transmission modes, directly on minute samples or on paint cross-sections embedded in resin. Characterisation of lead white pigments reveals variations in terms of composition, graininess and proportion of mineral phases. The present work enlightens the presence of lead white as differentiable main composition groups, which could be specific of a period, a know-how or a geographical origin. In this way, we aim at understanding the choices and the trading of pigments used to realise paintings during northern European Renaissance. (orig.)

Classification of lead white pigments using synchrotron radiation micro X-ray diffraction

Energy Technology Data Exchange (ETDEWEB)

Welcomme, E.; Walter, P.; Menu, M. [Centre de Recherche et de Restauration des Musees de France - CNRS UMR 171, Paris (France); Bleuet, P. [European Synchrotron Radiation Facility, BP 220, Grenoble Cedex (France); Hodeau, J.L.; Dooryhee, E.; Martinetto, P. [Institut Neel CNRS-UPR 503-1, 25, Av. des Martyrs, BP 166, Grenoble Cedex 9 (France)

2007-12-15

Lead white pigment was used and synthesised for cosmetic and artistic purposes since the antiquity. Ancient texts describe the various recipes, and preparation processes as well as locations of production. In this study, we describe the results achieved on several paint samples taken from Matthias Gruenewald's works. Gruenewald, who was active between 1503 and 1524, was a major painter at the beginning of the German Renaissance. Thanks to X-ray diffraction analysis using synchrotron radiation, it is possible to associate the composition of the paint samples with the masters ancient recipes. Different approaches were used, in reflection and transmission modes, directly on minute samples or on paint cross-sections embedded in resin. Characterisation of lead white pigments reveals variations in terms of composition, graininess and proportion of mineral phases. The present work enlightens the presence of lead white as differentiable main composition groups, which could be specific of a period, a know-how or a geographical origin. In this way, we aim at understanding the choices and the trading of pigments used to realise paintings during northern European Renaissance. (orig.)

Utilizing In Situ Directional Hyperspectral Measurements to Validate Bio-Indicator Simulations for a Corn Crop Canopy

Science.gov (United States)

Cheng, Yen-Ben; Middleton, Elizabeth M.; Huemmrich, Karl F.; Zhang, Qingyuan; Campbell, Petya K. E.; Corp, Lawrence A.; Russ, Andrew L.; Kustas, William P.

2010-01-01

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (psi) at three different view zenith angles (Theta (sub v)), and later used to validate model outputs. Field observations of photochemical reflectance index (PRI) for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both Theta (sub v) and psi . Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. Leaf area index (LAI) showed the most significant impact on MCRM-simulated PRI among the parameters

A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles

NARCIS (Netherlands)

Suomalainen, J.M.; Anders, N.S.; Iqbal, S.; Roerink, G.J.; Franke, G.J.; Wenting, P.F.M.; Hünniger, D.; Bartholomeus, H.; Becker, R.; Kooistra, L.

2014-01-01

During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of

Sensitizing pigment in the fly

International Nuclear Information System (INIS)

Vogt, K.; Kirschfeld, K.

1983-01-01

The sensitizing pigment hypothesis for the high UV sensitivity in fly photoreceptors (R1-6) is further substantiated by measurements of the polarisation sensitivity in the UV. The quantum yield of the energy transfer from sensitizing pigment to rhodopsin was estimated by electrophysiological measurements of the UV sensitivity and the rhabdomeric absorptance (at 490 nm) in individual receptor cells. The transfer efficiency is >=0.75 in receptors with an absorptance in the rhabdomeres of 0.55-0.95. This result suggests that the sensitizing pigment is bound in some way to the rhodopsin. A ratio of two molecules of sensitizing pigment per one rhodopsin is proposed. (orig.)

Raman Spectroscopy of Microbial Pigments

Science.gov (United States)

Edwards, Howell G. M.; Oren, Aharon

2014-01-01

Raman spectroscopy is a rapid nondestructive technique providing spectroscopic and structural information on both organic and inorganic molecular compounds. Extensive applications for the method in the characterization of pigments have been found. Due to the high sensitivity of Raman spectroscopy for the detection of chlorophylls, carotenoids, scytonemin, and a range of other pigments found in the microbial world, it is an excellent technique to monitor the presence of such pigments, both in pure cultures and in environmental samples. Miniaturized portable handheld instruments are available; these instruments can be used to detect pigments in microbiological samples of different types and origins under field conditions. PMID:24682303

The Hyperspectral Imager for the Coastal Ocean (HICO (trademark)) Provides a New View of the Coastal Ocean

Science.gov (United States)

2012-02-09

The calibrated data are then sent to NRL Stennis Space Center (NRL-SSC) for further processing using the NRL SSC Automated Processing System (APS...hyperspectral sensor in space we have not previously developed automated processing for hyperspectral ocean color data. The hyperspectral processing branch

Parallel exploitation of a spatial-spectral classification approach for hyperspectral images on RVC-CAL

Science.gov (United States)

Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.

2017-10-01

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Preparation of an anionic azo pigment-pillared layered double hydroxide and the thermo- and photostability of the resulting intercalated material

Science.gov (United States)

Guo, Shengchang; Li, Dianqing; Zhang, Weifeng; Pu, Min; Evans, David G.; Duan, Xue

2004-12-01

A large anionic pigment has been intercalated into a layered double hydroxide (LDH) host by ion-exchange of an Mg/Al LDH-nitrate precursor with a solution of C.I. Pigment Red 48:2 (the calcium salt of 4-((5-chloro-4-methyl-2-sulfophenyl)azo)-3-hydroxy-2-naphthalene-carboxylic acid), in ethane-1,2-diol. After intercalation of the pigment, the interlayer distance in the LDH increases from 0.86 to 1.72 nm. Infrared spectra and TG-DTA curves reveal the presence of a complex system of supramolecular host-guest interactions. The UV-visible diffuse reflectance spectra of C.I. Pigment Red 48:2 show marked changes after heating at 200 °C and above, whereas there are no significant changes in the spectra of the intercalated pigment after heating at temperatures up to 300 °C, showing that the thermostability is markedly enhanced by intercalation in the LDH host. The pigment-intercalated LDHs exhibits much higher photostability to UV light than the pristine pigment, in the case of both the pure solids and their composites with polypropylene, as shown by measurement of CIE 1976 L*a*b* color difference ( ΔE) values.

Effect of pigment concentration on fastness and color values of thermal and UV curable pigment printing

Science.gov (United States)

Baysal, Gulcin; Kalav, Berdan; Karagüzel Kayaoğlu, Burçak

2017-10-01

In the current study, it is aimed to determine the effect of pigment concentration on fastness and colour values of thermal and ultraviolet (UV) curable pigment printing on synthetic leather. For this purpose, thermal curable solvent-based and UV curable water-based formulations were prepared with different pigment concentrations (3, 5 and 7%) separately and applied by screen printing technique using a screen printing machine. Samples printed with solvent-based formulations were thermally cured and samples printed with water-based formulations were cured using a UV curing machine equipped with gallium and mercury (Ga/Hg) lamps at room temperature. The crock fastness values of samples printed with solvent-based formulations showed that increase in pigment concentration was not effective on both dry and wet crock fastness values. On the other hand, in samples printed with UV curable water-based formulations, dry crock fastness was improved and evaluated as very good for all pigment concentrations. However, increasing the pigment concentration affected the wet crock fastness values adversely and lower values were observed. As the energy level increased for each irradiation source, the fastness values were improved. In comparison with samples printed with solvent-based formulations, samples printed with UV curable water-based formulations yielded higher K/S values at all pigment concentrations. The results suggested that, higher K/S values can be obtained in samples printed with UV curable water-based formulations at a lower pigment concentration compared to samples printed with solvent-based formulations.

High-throughput optical system for HDES hyperspectral imager

Science.gov (United States)

Václavík, Jan; Melich, Radek; Pintr, Pavel; Pleštil, Jan

2015-01-01

Affordable, long-wave infrared hyperspectral imaging calls for use of an uncooled FPA with high-throughput optics. This paper describes the design of the optical part of a stationary hyperspectral imager in a spectral range of 7-14 um with a field of view of 20°×10°. The imager employs a push-broom method made by a scanning mirror. High throughput and a demand for simplicity and rigidity led to a fully refractive design with highly aspheric surfaces and off-axis positioning of the detector array. The design was optimized to exploit the machinability of infrared materials by the SPDT method and a simple assemblage.

On-line database of voltammetric data of immobilized particles for identifying pigments and minerals in archaeometry, conservation and restoration (ELCHER database).

Science.gov (United States)

Doménech-Carbó, Antonio; Doménech-Carbó, María Teresa; Valle-Algarra, Francisco Manuel; Gimeno-Adelantado, José Vicente; Osete-Cortina, Laura; Bosch-Reig, Francisco

2016-07-13

A web-based database of voltammograms is presented for characterizing artists' pigments and corrosion products of ceramic, stone and metal objects by means of the voltammetry of immobilized particles methodology. Description of the website and the database is provided. Voltammograms are, in most cases, accompanied by scanning electron microphotographs, X-ray spectra, infrared spectra acquired in attenuated total reflectance Fourier transform infrared spectroscopy mode (ATR-FTIR) and diffuse reflectance spectra in the UV-Vis-region. For illustrating the usefulness of the database two case studies involving identification of pigments and a case study describing deterioration of an archaeological metallic object are presented. Copyright © 2016 Elsevier B.V. All rights reserved.

Central posterior capsule pigmentation in a patient with pigment dispersion and previous ocular trauma: a case report.

Science.gov (United States)

Al-Mezaine, Hani S

2010-01-01

We report a 55-year-old man with unusually dense, unilateral central posterior capsule pigmentation associated with the characteristic clinical features of pigment dispersion syndrome, including a Krukenberg's spindle and dense trabecular pigmentation in both eyes. A history of an old blunt ocular trauma probably caused separation of the anterior hyaloid from the back of the lens, thereby creating an avenue by which pigment could reach the potential space of Berger's from the posterior chamber.

Spectral contribution of understory to forest reflectance in a boreal site: an analysis of EO-1 Hyperion data

Czech Academy of Sciences Publication Activity Database

Rautianien, M.; Lukeš, Petr

2015-01-01

Roč. 171, dec (2015), s. 98-104 ISSN 0034-4257 R&D Projects: GA MŠk(CZ) LO1415 Institutional support: RVO:67179843 Keywords : forest reflectance model * hyperspectral * boreal * leaf area index * understory Subject RIV: EH - Ecology, Behaviour Impact factor: 5.881, year: 2015

A system design for storing, archiving, and retrieving hyperspectral data

Science.gov (United States)

Dedecker, Ralph G.; Whittaker, Tom; Garcia, Raymond K.; Knuteson, Robert O.

2004-10-01

Hyperspectral data and products derived from instrumentation such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and the GOES-R Hyperspectral Environmental Suite (HES) will impose storage and data retrieval requirements that far exceed the demands of earlier generation remote sensing instrumentation used for atmospheric science research. A new architecture designed to address projected real time and research needs is undergoing prototype design and development. The system is designed using proven aspects of distributed data storage networks, descriptive metadata associated with stored files, data cataloging and database search schemes, and a data delivery approach that obeys accepted standards. Preliminary implementation and testing of some components of this architecture indicate that the design approach shows promise of an improved method for storage and library functionality for the data volumes associated with operational hyperspectral instrumentation.

Sparse-Based Modeling of Hyperspectral Data

DEFF Research Database (Denmark)

Calvini, Rosalba; Ulrici, Alessandro; Amigo Rubio, Jose Manuel

2016-01-01

One of the main issues of hyperspectral imaging data is to unravel the relevant, yet overlapped, huge amount of information contained in the spatial and spectral dimensions. When dealing with the application of multivariate models in such high-dimensional data, sparsity can improve...

GPU implementation of discrete particle swarm optimization algorithm for endmember extraction from hyperspectral image

Science.gov (United States)

Yu, Chaoyin; Yuan, Zhengwu; Wu, Yuanfeng

2017-10-01

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

The French proposal for a high spatial resolution Hyperspectral mission

Science.gov (United States)

Carrère, Véronique; Briottet, Xavier; Jacquemoud, Stéphane; Marion, Rodolphe; Bourguignon, Anne; Chami, Malik; Chanussot, Jocelyn; Chevrel, Stéphane; Deliot, Philippe; Dumont, Marie; Foucher, Pierre-Yves; Gomez, Cécile; Roman-Minghelli, Audrey; Sheeren, David; Weber, Christiane; Lefèvre, Marie-José; Mandea, Mioara

2014-05-01

More than 25 years of airborne imaging spectroscopy and spaceborne sensors such as Hyperion or HICO have clearly demonstrated the ability of such a remote sensing technique to produce value added information regarding surface composition and physical properties for a large variety of applications. Scheduled missions such as EnMAP and PRISMA prove the increased interest of the scientific community for such a type of remote sensing data. In France, a group of Science and Defence users of imaging spectrometry data (Groupe de Synthèse Hyperspectral, GSH) established an up-to-date review of possible applications, define instrument specifications required for accurate, quantitative retrieval of diagnostic parameters, and identify fields of application where imaging spectrometry is a major contribution. From these conclusions, CNES (French Space Agency) decided a phase 0 study for an hyperspectral mission concept, named at this time HYPXIM (HYPerspectral-X IMagery), the main fields of applications are vegetation biodiversity, coastal and inland waters, geosciences, urban environment, atmospheric sciences, cryosphere and Defence. Results pointed out applications where high spatial resolution was necessary and would not be covered by the other foreseen hyperspectral missions. The phase A started at the beginning of 2013 based on the following HYPXIM characteristics: a hyperspectral camera covering the [0.4 - 2.5 µm] spectral range with a 8 m ground sampling distance (GSD) and a PAN camera with a 1.85 m GSD, onboard a mini-satellite platform. This phase A is currently stopped due to budget constraints. Nevertheless, the Science team is currently focusing on the preparation for the next CNES prospective meeting (March, 2014), an important step for the future of the mission. This paper will provide an update of the status of this mission and of new results obtained by the Science team.

Can porosity affect the hyperspectral signature of sandy landscapes?

Science.gov (United States)

Baranoski, Gladimir V. G.; Kimmel, Bradley W.

2017-10-01

Porosity is a fundamental property of sand deposits found in a wide range of landscapes, from beaches to dune fields. As a primary determinant of the density and permeability of sediments, it represents a central element in geophysical studies involving basin modeling and coastal erosion as well as geoaccoustics and geochemical investigations aiming at the understanding of sediment transport and water diffusion properties of sandy landscapes. These applications highlight the importance of obtaining reliable porosity estimations, which remains an elusive task, notably through remote sensing. In this work, we aim to contribute to the strengthening of the knowledge basis required for the development of new technologies for the remote monitoring of environmentally-triggered changes in sandy landscapes. Accordingly, we employ an in silico investigation approach to assess the effects of porosity variations on the reflectance of sandy landscapes in the visible and near-infrared spectral domains. More specifically, we perform predictive computer simulations using SPLITS, a hyperspectral light transport model for particulate materials that takes into account actual sand characterization data. To the best of our knowledge, this work represents the first comprehensive investigation relating porosity to the reflectance responses of sandy landscapes. Our findings indicate that the putative dependence of these responses on porosity may be considerably less pronounced than its dependence on other properties such as grain size and shape. Hence, future initiatives for the remote quantification of porosity will likely require reflectance sensors with a high degree of sensitivity.

A light-weight hyperspectral mapping system for unmanned aerial vehicles - The first results

NARCIS (Netherlands)

Suomalainen, Juha; Anders, Niels; Iqbal, Shahzad; Franke, Jappe; Wenting, Philip; Bartholomeus, Harm; Becker, Rolf; Kooistra, Lammert

2017-01-01

Research opportunities using UAV remote sensing techniques are limited by the payload of the platform. Therefore small UAV's are typically not suitable for hyperspectral imaging due to the weight of the mapping system. In this research, we are developing a light-weight hyperspectral mapping system

Common hyperspectral image database design

Science.gov (United States)

Tian, Lixun; Liao, Ningfang; Chai, Ali

2009-11-01

This paper is to introduce Common hyperspectral image database with a demand-oriented Database design method (CHIDB), which comprehensively set ground-based spectra, standardized hyperspectral cube, spectral analysis together to meet some applications. The paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies, some data mining ideas and functions were associated into CHIDB to make it more suitable to serve in agriculture, geological and environmental areas. A broad range of data from multiple regions of the electromagnetic spectrum is supported, including ultraviolet, visible, near-infrared, thermal infrared, and fluorescence. CHIDB is based on dotnet framework and designed by MVC architecture including five main functional modules: Data importer/exporter, Image/spectrum Viewer, Data Processor, Parameter Extractor, and On-line Analyzer. The original data were all stored in SQL server2008 for efficient search, query and update, and some advance Spectral image data Processing technology are used such as Parallel processing in C#; Finally an application case is presented in agricultural disease detecting area.

Gene expression analysis of zebrafish melanocytes, iridophores, and retinal pigmented epithelium reveals indicators of biological function and developmental origin.

Directory of Open Access Journals (Sweden)

Charles W Higdon

Full Text Available In order to facilitate understanding of pigment cell biology, we developed a method to concomitantly purify melanocytes, iridophores, and retinal pigmented epithelium from zebrafish, and analyzed their transcriptomes. Comparing expression data from these cell types and whole embryos allowed us to reveal gene expression co-enrichment in melanocytes and retinal pigmented epithelium, as well as in melanocytes and iridophores. We found 214 genes co-enriched in melanocytes and retinal pigmented epithelium, indicating the shared functions of melanin-producing cells. We found 62 genes significantly co-enriched in melanocytes and iridophores, illustrative of their shared developmental origins from the neural crest. This is also the first analysis of the iridophore transcriptome. Gene expression analysis for iridophores revealed extensive enrichment of specific enzymes to coordinate production of their guanine-based reflective pigment. We speculate the coordinated upregulation of specific enzymes from several metabolic pathways recycles the rate-limiting substrate for purine synthesis, phosphoribosyl pyrophosphate, thus constituting a guanine cycle. The purification procedure and expression analysis described here, along with the accompanying transcriptome-wide expression data, provide the first mRNA sequencing data for multiple purified zebrafish pigment cell types, and will be a useful resource for further studies of pigment cell biology.

Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

Directory of Open Access Journals (Sweden)

Antonio Plaza

2010-01-01

Full Text Available Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

Directory of Open Access Journals (Sweden)

Paz Abel

2010-01-01

Full Text Available Abstract Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

Discrimination methods for biological contaminants in fresh-cut lettuce based on VNIR and NIR hyperspectral imaging

Science.gov (United States)

Mo, Changyeun; Kim, Giyoung; Kim, Moon S.; Lim, Jongguk; Lee, Seung Hyun; Lee, Hong-Seok; Cho, Byoung-Kwan

2017-09-01

The rapid detection of biological contaminants such as worms in fresh-cut vegetables is necessary to improve the efficiency of visual inspections carried out by workers. Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms in fresh-cut lettuce. The optimal wavebands that can detect worms in fresh-cut lettuce were investigated for each type of HSI using one-way ANOVA. Worm-detection imaging algorithms for VNIR and NIR imaging exhibited prediction accuracies of 97.00% (RI547/945) and 100.0% (RI1064/1176, SI1064-1176, RSI-I(1064-1173)/1064, and RSI-II(1064-1176)/(1064+1176)), respectively. The two HSI techniques revealed that spectral images with a pixel size of 1 × 1 mm or 2 × 2 mm had the best classification accuracy for worms. The results demonstrate that hyperspectral reflectance imaging techniques have the potential to detect worms in fresh-cut lettuce. Future research relating to this work will focus on a real-time sorting system for lettuce that can simultaneously detect various defects such as browning, worms, and slugs.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

Directory of Open Access Journals (Sweden)

Heekang Kim

2016-07-01

Full Text Available This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD or Complementary metal-Oxide-Semiconductor (CMOS camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC, the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs, High-intensity discharge (HID, and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM, Spectral Correlation Mapper (SCM, and Euclidean Distance Mapper (EDM. The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen.

Upscaling from leaf to canopy chlorophyll/carotenoid pigment based vegetation indices reveal phenology of photosynthesis in temperate evergreen and deciduous trees

Science.gov (United States)

Wong, C. Y.; Bhathena, Y.; Arain, M. A.; Ensminger, I.

2017-12-01

Optically derived vegetation indices have been developed to provide information about plant status including photosynthetic activity. They reflect changes in leaf pigments, which vary seasonally in pigment composition, enabling them to be used as a proxy of photosynthetic phenology. Important pigments in photosynthetic activity are carotenoids and chlorophylls, which are associated with light harvesting and energy dissipation. In temperate forests, which consist of deciduous and evergreen trees, there are difficulties resolving evergreen phenology using the most widely used index, the normalized difference vegetation index (NDVI). NDVI works well in deciduous trees, which exhibit a "visible" phenological process of leaf growth in the spring, and leaf senescence and abscission in the autumn. Evergreen conifers stay green year-round and utilize "invisible" changes of overwintering pigment composition that NDVI cannot resolve, so carotenoid pigment sensitive vegetation indices have been suggested for evergreens. The aim of this study was to evaluate carotenoid based vegetation indices over the chlorophyll sensitive NDVI. For this purpose, we evaluated the greenness index, NDVI, and carotenoid pigment sensitive indices: photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) in red maple, white oak and eastern white pine for two years. We also measured leaf gas exchange and pigment concentrations. We observed that NDVI correlated with photosynthetic activity in deciduous trees, whereas PRI and CCI correlated with photosynthesis across both evergreen and deciduous trees. This pattern was consistent, upscaling from leaf- to canopy-scales indicating that the mechanisms involved in winter acclimation can be resolved at larger spatial scales. PRI and CCI detected seasonal changes in carotenoids and chlorophylls linked to photoprotection and are suitable as a proxy of photosynthetic activity. These findings have implications to improve our use and

Preparation of spherical fine particulate pigments within water-in-oil emulsions and their properties. (II). ; Formation mechanism and characteristic of spherical fine particulate pigment of tartrazine. W/O emulsion wo mochiita kyujo biryushi ganryo no chosei to seishitsu(dai 2 ho). ; Kiiro 4 go kyujo biryushi ganryo no seisei kiko to tokusei

Energy Technology Data Exchange (ETDEWEB)

Imai, T.; Iwano, K.; Hotta, H.; Takano, S.; Tsutsumi, H. (Kao Corporation, Tokyo (Japan))

1991-12-20

The previous report explained that an excellent spherical particulate pigment with a grain size of 0.5 mm or less can be obtained by preparing multinuclear aluminum lakes from acidic dyes and multinuclear aluminum salt using water droplets in a W/O emulsion as reaction fields. This paper describes preparing pigments varying the charging concentrations of the pigments in a W/O emulsion and the droplet particle size to discuss the mechanism of forming the pigments. As a result, it was found that the particle sizes in the produced pigments have a clear correlation with the charging concentrations of the pigments and the droplet particle sizes in the W/O emulsion. A pigment produced in the W/O emulsion forms only in its own droplets, and reflects its particle sizes. Films dispersed with pigments having different particle sizes were prepared to discuss their tinting abilities, whereas it was clarified that the smaller the particle size, the higher the tinting ability and the higher saturation in colored paint films. 6 refs., 9 figs., 3 tabs.

ALGORITMA ESTIMASI KANDUNGAN KLOROFIL TANAMAN PADI DENGAN DATA AIRBORNE HYPERSPECTRAL

Directory of Open Access Journals (Sweden)

Abdi Sukmono

2015-02-01

Full Text Available Klorofil merupakan pigmen yang paling penting dalam proses fotosintesis. Tanaman sehat yang mampu tumbuh maksimum umumnya  memiliki jumlah klorofil yang lebih besar daripada tanaman yang tidak sehat. Dalam Estimasi kandungan klorofil tanaman padi dengan airborne hyperspectral dibutuhkan algoritma khusus untuk mendaaptkan akurasi yang baik. Objek dari penelitian ini mengembangkan reflektan in situ menjadi model algoritma   estimasi kandungan klorofil tanaman padi untuk airborne hyperspectral.  Dalam penelitian ini beberapa indeks vegetasi seperti normalized difference vegetation index (NDVI, modified simple ratio (MSR  , modified/transformed chlorophyll absorption ratio index (MCARI, TCARI dan bentuk integrasi (MCARI/OSAVI and TCARI/OSAVI digunakan untuk membentuk model estimasi dengan metode regresi linear. Selain itu juga digunakan  Blue/Green/Yellow/Red Edge Absorption Clhorophyll Index. Dari proses regresi di dapatkan tiga ground model yang mempunyai korelasi kuat (R2≥0.5 terhadap klorofil tanaman padi. Ketiga model tersebut yaitu MSR (705,750 dengan R2 sebesar 0.51, TCARI/OSAVI (705, 750 dengan R2 sebesar 0.52 dan REACL 2 dengan R2 sebesar 0.57. Dari ketiga tersebut dipilih groun model terbaik REACL 2 untuk di upscalling ke model algoritma airborne hyperspectral.  Pembentukan algoritma dengan data airborne hyperspectral sensor Hymap dan REACL 2 menghasilkan model algoritma ( Klorofil (SPAD unit = 3.031((B22-B18/(B18-B13 + 31.596 dengan R2 sebesar 0.78

AN EXTENDED SPECTRAL–SPATIAL CLASSIFICATION APPROACH FOR HYPERSPECTRAL DATA

Directory of Open Access Journals (Sweden)

D. Akbari

2017-11-01

Full Text Available In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1 unsupervised feature extraction methods including principal component analysis (PCA, independent component analysis (ICA, and minimum noise fraction (MNF; (2 supervised feature extraction including decision boundary feature extraction (DBFE, discriminate analysis feature extraction (DAFE, and nonparametric weighted feature extraction (NWFE; (3 genetic algorithm (GA. The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Comparative study between fundus autofluorescence and red reflectance imaging of choroidal nevi using ultra-wide-field scanning laser ophthalmoscopy.

Science.gov (United States)

Zapata, Miguel Angel; Leila, Mahmoud; Teixidor, Teresa; Garcia-Arumi, Jose

2015-06-01

To explore the utility of fundus autofluorescence (FAF) and red reflectance (RR) imaging using ultra-wide-field scanning laser ophthalmoscope in choroidal nevi. Retrospective observational case study reviewing clinical data, color, FAF, and RR images of patients with choroidal nevi and comparing the findings. The ultra-wide-field scanning laser ophthalmoscope uses green laser 532 nm and red laser 633 nm that enabled FAF and RR imaging, respectively in separate channels. Superimposition of both images yielded a composite color image. The study included 46 eyes of 45 patients. Nevi were unilateral in 44 patients (98%). Forty-one nevi (89.1%) were located temporally between the macula and the equator. All nevi (100%) were deeply pigmented. The most frequent surface changes were lipofuscin pigments, zones of retinal pigment epithelium atrophy, and retinal pigment epithelium pigment clumps in 31 (67.3%), 18 (39.1%), and 8 eyes (17.3%), respectively. Color photographs were superior to FAF in detecting nevus boundaries and surface changes. Red reflectance correlated strongly with color images, although the nevus boundaries and surface changes were better delineated in RR mode. Red reflectance was superior to FAF in delineating the boundaries and surface changes of the nevus; clear visibility (3+) for RR versus no or poor visibility (0/1+) for FAF. Nevertheless, the areas of retinal pigment epithelium atrophy were better delineated in FAF mode; clear visibility (3+) for FAF versus poor visibility (1+) for FAF. Red reflectance imaging is more sensitive than conventional photography for follow-up of choroidal nevi. Fundus autofluorescence should be considered only as a complementary tool to RR imaging.

The visual pigment cyanide effect.

Science.gov (United States)

Crescitelli, F; Karvaly, B

1989-12-01

The visual pigment of the Tokay gecko (Gekko gekko) with its in situ absorption maximum at 521 nm has its spectral position at 500 to 505 nm when chloride-deficient digitonin is used for the extraction. In this case the addition of chloride or bromide to the extract restores the maximum to 521 nm. This property, characteristic of gecko pigments in general, does not occur with any of the rhodopsins that have been tested. Simple salts of cyanide, a pseudohalogenoid with an ionic radius close to those of chloride and bromide and/or its hydrolysis product attacks both this gecko pigment and rhodopsins in the dark. This is seen as a slow thermal loss of photopigment if (sodium) cyanide is present at concentrations above 40 mM for the gecko pigment and 150 mM for the rhodopsins of the midshipman (Porichthys notatus) and of the frog (Rana pipiens). In all cases the loss of the photopigment is accompanied by the appearance of a spectral product with maximum absorption at about 340 nm. Cyanide addition has no effect on the photosensitivity of the native pigments and neither does it alter, as do chloride, bromide and other anions, the spectral absorbance curve. The spectral product at 340 nm also appears when the visual pigments are photolyzed in the presence of cyanide salts below the threshold concentrations given above. Incubation of digitonin-solubilized all-trans-retinal with (sodium) cyanide leads to a reaction product with absorption spectrum similar to that obtained with visual pigments under comparable conditions.(ABSTRACT TRUNCATED AT 250 WORDS)

Is spectral reflectance of the face a reliable biometric?

Science.gov (United States)

Uzair, Muhammad; Mahmood, Arif; Shafait, Faisal; Nansen, Christian; Mian, Ajmal

2015-06-15

Over a decade ago, Pan et al. [IEEE TPAMI 25, 1552 (2003)] performed face recognition using only the spectral reflectance of the face at six points and reported around 95% recognition rate. Since their database is private, no one has been able to replicate these results. Moreover, due to the unavailability of public datasets, there has been no detailed study in the literature on the viability of facial spectral reflectance for person identification. In this study, we introduce a new public database of facial spectral reflectance profiles measured with a high precision spectrometer. For each of the 40 subjects, spectral reflectance was measured at the same six points as Pan et al. [IEEE TPAMI 25, 1552 (2003)] in multiple sessions and with time lapse. Furthermore, we sample the facial spectral reflectance from two public hyperspectral face image datasets and analyzed the data using state of the art face classification techniques. The best performing classifier achieved the maximum rank-1 identification rate of 53.8%. We conclude that facial spectral reflectance alone is not a reliable biometric for unconstrained face recognition.

Reconfigurable Hardware for Compressing Hyperspectral Image Data

Science.gov (United States)

Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua

2010-01-01

High-speed, low-power, reconfigurable electronic hardware has been developed to implement ICER-3D, an algorithm for compressing hyperspectral-image data. The algorithm and parts thereof have been the topics of several NASA Tech Briefs articles, including Context Modeler for Wavelet Compression of Hyperspectral Images (NPO-43239) and ICER-3D Hyperspectral Image Compression Software (NPO-43238), which appear elsewhere in this issue of NASA Tech Briefs. As described in more detail in those articles, the algorithm includes three main subalgorithms: one for computing wavelet transforms, one for context modeling, and one for entropy encoding. For the purpose of designing the hardware, these subalgorithms are treated as modules to be implemented efficiently in field-programmable gate arrays (FPGAs). The design takes advantage of industry- standard, commercially available FPGAs. The implementation targets the Xilinx Virtex II pro architecture, which has embedded PowerPC processor cores with flexible on-chip bus architecture. It incorporates an efficient parallel and pipelined architecture to compress the three-dimensional image data. The design provides for internal buffering to minimize intensive input/output operations while making efficient use of offchip memory. The design is scalable in that the subalgorithms are implemented as independent hardware modules that can be combined in parallel to increase throughput. The on-chip processor manages the overall operation of the compression system, including execution of the top-level control functions as well as scheduling, initiating, and monitoring processes. The design prototype has been demonstrated to be capable of compressing hyperspectral data at a rate of 4.5 megasamples per second at a conservative clock frequency of 50 MHz, with a potential for substantially greater throughput at a higher clock frequency. The power consumption of the prototype is less than 6.5 W. The reconfigurability (by means of reprogramming) of

High efficiency non-viral transfection of retinal and iris pigment epithelial cells with pigment epithelium-derived factor.

Science.gov (United States)

Thumann, G; Stöcker, M; Maltusch, C; Salz, A K; Barth, S; Walter, P; Johnen, S

2010-02-01

Transplantation of pigment epithelial cells in patients with age-related macular degeneration and Parkinson's disease has the potential to improve functional rehabilitation. Genetic modification of cells before transplantation may allow the delivery of neuroprotective factors to achieve functional improvement. As transplantation of cells modified using viral vectors is complicated by the possible dissemination of viral particles and severe immune reactions, we have explored non-viral methods to insert genetic material in pigment epithelial cells. Using lipofection or nucleofection ARPE-19 cells, freshly isolated and primary retinal and iris pigment epithelial (IPE) cells were transfected with plasmids encoding green fluorescent protein (GFP) and with three plasmids encoding recombinant pigment epithelium-derived factor (PEDF) and GFP. Transfection efficiency was evaluated by fluorescence microscopy and stability of protein expression by immunoblotting. Pigment epithelial cells were successfully transfected with plasmid encoding GFP. Expression of GFP in ARPE-19 was transient, but was observed for up to 1 year in IPE cells. Analysis of pigment epithelial cells transfected with PEDF plasmids revealed that PEDF fusion proteins were successfully expressed and functionally active. In conclusion, efficient transfer of genetic information in pigment epithelial cells can be achieved using non-viral transfection protocols.

Central posterior capsule pigmentation in a patient with pigment dispersion and previous ocular trauma: A case report

Directory of Open Access Journals (Sweden)

Al-Mezaine Hani

2010-01-01

Full Text Available We report a 55-year-old man with unusually dense, unilateral central posterior capsule pigmentation associated with the characteristic clinical features of pigment dispersion syndrome, including a Krukenberg′s spindle and dense trabecular pigmentation in both eyes. A history of an old blunt ocular trauma probably caused separation of the anterior hyaloid from the back of the lens, thereby creating an avenue by which pigment could reach the potential space of Berger′s from the posterior chamber.

Processing of hyperspectral medical images applications in dermatology using Matlab

CERN Document Server

Koprowski, Robert

2017-01-01

This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.

Biomarker pigment signatures in Cochin back water system - A tropical estuary south west coast of India

Science.gov (United States)

Aneeshkumar, N.; Sujatha, C. H.

2012-03-01

Sedimentary biomarker pigments around Cochin estuary situated in the southwest coast of India were determined by HPLC. Fucoxanthin, an indicator of diatom was observed to be the most abundant carotenoid pigment in the estuary. Dinoflagellate derived carotenoid pigment peridinin was confined in the southern part of estuary and zeaxanthin pigment indicative of cyanobacteria were more found in sites influenced by anthropogenic activities. One compound having close similarity to fucoxanthin was also detected. Alloxanthin (cryptophyceae), chl b (green algae), canthaxanthin, neoxanthin, lutein and peridinin isomer were also detected by spectra and corresponding algal class were identified. The highest concentration of chl a (11.01 μg g-1) found near to the anthropogenic affected area while the lowest chl a (0.65 μg g-1) was recorded in industrial area. Degradation products of chl a, such as pheophorbide and pheophytin were observed and principal mode of mechanism of degradation were derived. Higher pheopigments content than chl a, reflects a density trapping of dead cells and early degradation of phytopigments from grazing activities.

Excitation-scanning hyperspectral imaging as a means to discriminate various tissues types

Science.gov (United States)

Deal, Joshua; Favreau, Peter F.; Lopez, Carmen; Lall, Malvika; Weber, David S.; Rich, Thomas C.; Leavesley, Silas J.

2017-02-01

Little is currently known about the fluorescence excitation spectra of disparate tissues and how these spectra change with pathological state. Current imaging diagnostic techniques have limited capacity to investigate fluorescence excitation spectral characteristics. This study utilized excitation-scanning hyperspectral imaging to perform a comprehensive assessment of fluorescence spectral signatures of various tissues. Immediately following tissue harvest, a custom inverted microscope (TE-2000, Nikon Instruments) with Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) were used to acquire hyperspectral image data from each sample. Scans utilized excitation wavelengths from 340 nm to 550 nm in 5 nm increments. Hyperspectral images were analyzed with custom Matlab scripts including linear spectral unmixing (LSU), principal component analysis (PCA), and Gaussian mixture modeling (GMM). Spectra were examined for potential characteristic features such as consistent intensity peaks at specific wavelengths or intensity ratios among significant wavelengths. The resultant spectral features were conserved among tissues of similar molecular composition. Additionally, excitation spectra appear to be a mixture of pure endmembers with commonalities across tissues of varied molecular composition, potentially identifiable through GMM. These results suggest the presence of common autofluorescent molecules in most tissues and that excitationscanning hyperspectral imaging may serve as an approach for characterizing tissue composition as well as pathologic state. Future work will test the feasibility of excitation-scanning hyperspectral imaging as a contrast mode for discriminating normal and pathological tissues.

[Mahalanobis distance based hyperspectral characteristic discrimination of leaves of different desert tree species].

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Lin, Hai-jun; Zhang, Hui-fang; Gao, Ya-qi; Li, Xia; Yang, Fan; Zhou, Yan-fei

2014-12-01

The hyperspectral reflectance of Populus euphratica, Tamarix hispida, Haloxylon ammodendron and Calligonum mongolicum in the lower reaches of Tarim River and Turpan Desert Botanical Garden was measured by using the HR-768 field-portable spectroradiometer. The method of continuum removal, first derivative reflectance and second derivative reflectance were used to deal with the original spectral data of four tree species. The method of Mahalanobis Distance was used to select the bands with significant differences in the original spectral data and transform spectral data to identify the different tree species. The progressive discrimination analyses were used to test the selective bands used to identify different tree species. The results showed that The Mahalanobis Distance method was an effective method in feature band extraction. The bands for identifying different tree species were most near-infrared bands. The recognition accuracy of four methods was 85%, 93.8%, 92.4% and 95.5% respectively. Spectrum transform could improve the recognition accuracy. The recognition accuracy of different research objects and different spectrum transform methods were different. The research provided evidence for desert tree species classification, monitoring biodiversity and the analysis of area in desert by using large scale remote sensing method.

Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images

Directory of Open Access Journals (Sweden)

Fenghua Huang

2014-01-01

Full Text Available To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1 different radial basis kernel functions (RBFs are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF is proposed by combining them in a weighted manner; (2 the binary decision tree-based multiclass SMO (BDT-SMO is used in the classification of hyperspectral fused images; (3 experiments are carried out, where the single radial basis function- (SRBF- based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA are used to optimize the kernel parameters of the classifiers. The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.

Detecting brain tumor in pathological slides using hyperspectral imaging.

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Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) is an emerging technology for medical diagnosis. This research work presents a proof-of-concept on the use of HSI data to automatically detect human brain tumor tissue in pathological slides. The samples, consisting of hyperspectral cubes collected from 400 nm to 1000 nm, were acquired from ten different patients diagnosed with high-grade glioma. Based on the diagnosis provided by pathologists, a spectral library of normal and tumor tissues was created and processed using three different supervised classification algorithms. Results prove that HSI is a suitable technique to automatically detect high-grade tumors from pathological slides.