Moughal, T A
Classification of land cover hyperspectral images is a very challenging task due to the unfavourable ratio between the number of spectral bands and the number of training samples. The focus in many applications is to investigate an effective classifier in terms of accuracy. The conventional multiclass classifiers have the ability to map the class of interest but the considerable efforts and large training sets are required to fully describe the classes spectrally. Support Vector Machine (SVM) is suggested in this paper to deal with the multiclass problem of hyperspectral imagery. The attraction to this method is that it locates the optimal hyper plane between the class of interest and the rest of the classes to separate them in a new high-dimensional feature space by taking into account only the training samples that lie on the edge of the class distributions known as support vectors and the use of the kernel functions made the classifier more flexible by making it robust against the outliers. A comparative study has undertaken to find an effective classifier by comparing Support Vector Machine (SVM) to the other two well known classifiers i.e. Maximum likelihood (ML) and Spectral Angle Mapper (SAM). At first, the Minimum Noise Fraction (MNF) was applied to extract the best possible features form the hyperspectral imagery and then the resulting subset of the features was applied to the classifiers. Experimental results illustrate that the integration of MNF and SVM technique significantly reduced the classification complexity and improves the classification accuracy.
Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z
The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance
Full Text Available This paper develops a new approach to band subset selection (BSS for hyperspectral image classification (HSIC which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS, rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS, as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ and successive (SC algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.
Yang, Huizhen; Gao, Feng; Dong, Junyu; Yang, Yang
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.
Chang, H.; Wang, T.; Fang, H.; Su, Y.
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.
Williams, Paul; Kucheryavskiy, Sergey V.
NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....
Full Text Available As a widely used classifier, sparse representation classification (SRC has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., ℓ 2 -norm can be used to regularize the coding coefficients, except for the sparsity ℓ 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1 the coefficient-level regularization strategy, and (2 the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.
. In the present work a classification method that combines classic image classification approach and MIA is proposed. The basic idea is to group all pixels and calculate spectral properties of the pixel group to be used further as a vector of predictors for calibration and class prediction. The grouping can...... be done with mathematical morphology methods applied to a score image where objects are well separated. In the case of small overlapping a watershed transformation can be applied to disjoint the objects. The method has been tested on several simulated and real cases and showed good results and significant...... improvements in comparison with a standard MIA approach. The results as well as method details will be reported....
Full Text Available The emergence of lightweight full-frame hyperspectral cameras is destined to enable autonomous search vehicles in the air, on the ground and in water. Self-contained and long-endurance systems will yield important new applications, for example, in emergency response and the timely identification of environmental hazards. One missing capability is rapid classification of hyperspectral scenes so that search vehicles can immediately take actions to verify potential targets. Onsite verifications minimise false positives and preclude the expense of repeat missions. Verifications will require enhanced image quality, which is achievable by either moving closer to the potential target or by adjusting the optical system. Such a solution, however, is currently impractical for small mobile platforms with finite energy sources. Rapid classifications with current methods demand large computing capacity that will quickly deplete the on-board battery or fuel. To develop the missing capability, the authors propose a low-complexity hyperspectral image classifier that approaches the performance of prevalent classifiers. This research determines that the new method will require at least 19-fold less computing capacity than the prevalent classifier. To assess relative performances, the authors developed a benchmark that compares a statistic of library endmember separability in their respective feature spaces.
Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.
Full Text Available There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.
Gu, M.; Lyu, S.; Hou, M.; Ma, S.; Gao, Z.; Bai, S.; Zhou, P.
There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.
Edelman, Gerda; Lopatka, Martin; Aalders, Maurice
The general procedure followed in the examination of ecstasy tablets for profiling purposes includes a color description, which depends highly on the observers' perception. This study aims to provide objective quantitative color information using visible hyperspectral imaging. Both self-manufactured
Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.
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.
Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.
Xie, Weiying; Li, Yunsong; Ge, Chiru
Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.
Full Text Available Independent component analysis combined with various strategies for cross-validation, uncertainty estimates by jack-knifing and critical Hotelling’s T2 limits estimation, proposed in this paper, is used for classification purposes in hyperspectral images. To the best of our knowledge, the combined approach of methods used in this paper has not been previously applied to hyperspectral imaging analysis for interpretation and classification in the literature. The data analysis performed here aims to distinguish between four different types of plastics, some of them containing brominated flame retardants, from their near infrared hyperspectral images. The results showed that the method approach used here can be successfully used for unsupervised classification. A comparison of validation approaches, especially leave-one-out cross-validation and regions of interest scheme validation is also evaluated.
Yang, Chun-Chieh; Jun, Won; Kim, Moon S.; Chao, Kaunglin; Kang, Sukwon; Chan, Diane E.; Lefcourt, Alan
This paper reported the development of hyperspectral fluorescence imaging system using ultraviolet-A excitation (320-400 nm) for detection of bovine fecal contaminants on the abaxial and adaxial surfaces of romaine lettuce and baby spinach leaves. Six spots of fecal contamination were applied to each of 40 lettuce and 40 spinach leaves. In this study, the wavebands at 666 nm and 680 nm were selected by the correlation analysis. The two-band ratio, 666 nm / 680 nm, of fluorescence intensity was used to differentiate the contaminated spots from uncontaminated leaf area. The proposed method could accurately detect all of the contaminated spots.
Chang, Lan; Zhang, Mengmeng; Li, Wei
A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.
images. In experiment section, the improved performance of HSI classification technique, K-FKT, has been tested comparing other methods such as the classical FKT and three types of support vector machines (SVMs.
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.
Kucheryavskiy, Sergey V.; Williams, Paul
Classification of objects (such as tablets, cereals, fruits, etc.) is one of the very important applications of hyperspectral imaging and image analysis. Quite often, a hyperspectral image is represented and analyzed just as a bunch of spectra without taking into account spatial information about...... the pixels, which makes classification objects inefficient. Recently, several methods, which combine spectral and spatial information, has been also developed and this approach becomes more and more wide-spread. The methods use local rank, topology, spectral features calculated for separate objects and other...... spatial characteristics. In this work we would like to show several improvements to the classification method, which utilizes spectral features calculated for individual objects . The features are based (in general) on descriptors of spatial patterns of individual object’s pixels in a common principal...
Jamshidpour, N.; Homayouni, S.; Safari, A.
Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.
Full Text Available Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.
Zhang, Feng-zhe; Yang, Xia
In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.
Full Text Available The computational procedure of hyperspectral image (HSI is extremely complex, not only due to the high dimensional information, but also due to the highly correlated data structure. The need of effective processing and analyzing of HSI has met many difficulties. It has been evidenced that dimensionality reduction has been found to be a powerful tool for high dimensional data analysis. Local Fisher’s liner discriminant analysis (LFDA is an effective method to treat HSI processing. In this paper, a novel approach, called PD-LFDA, is proposed to overcome the weakness of LFDA. PD-LFDA emphasizes the probability distribution (PD in LFDA, where the maximum distance is replaced with local variance for the construction of weight matrix and the class prior probability is applied to compute the affinity matrix. The proposed approach increases the discriminant ability of the transformed features in low dimensional space. Experimental results on Indian Pines 1992 data indicate that the proposed approach significantly outperforms the traditional alternatives.
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.
Full Text Available In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM. To show the superior performance of the proposed approach, conventional support vector machines (SVMs and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC, joint distribution adaptation (JDA, and joint transfer matching (JTM, are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA, randomized nonlinear principal component analysis (rPCA, factor analysis (FA and non-negative matrix factorization (NNMF are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.
Lazcano, R.; Madroñal, D.; Fabelo, H.; Ortega, S.; Salvador, R.; Callicó, G. M.; Juárez, E.; Sanz, C.
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.
Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.
Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
Ma, Ling; Lu, Guolan; Wang, Dongsheng; Wang, Xu; Chen, Zhuo Georgia; Muller, Susan; Chen, Amy; Fei, Baowei
Hyperspectral imaging (HSI) is an emerging imaging modality that can provide a noninvasive tool for cancer detection and image-guided surgery. HSI acquires high-resolution images at hundreds of spectral bands, providing big data to differentiating different types of tissue. We proposed a deep learning based method for the detection of head and neck cancer with hyperspectral images. Since the deep learning algorithm can learn the feature hierarchically, the learned features are more discriminative and concise than the handcrafted features. In this study, we adopt convolutional neural networks (CNN) to learn the deep feature of pixels for classifying each pixel into tumor or normal tissue. We evaluated our proposed classification method on the dataset containing hyperspectral images from 12 tumor-bearing mice. Experimental results show that our method achieved an average accuracy of 91.36%. The preliminary study demonstrated that our deep learning method can be applied to hyperspectral images for detecting head and neck tumors in animal models.
Liu, Q J; Jing, L H; Wang, L M; Lin, Q Z
Support Vector Machine (SVM) has been proved to be suitable for classification of remote sensing image and proposed to overcome the Hughes phenomenon. Hyper-spectral sensors are intrinsically designed to discriminate among a broad range of land cover classes which may lead to high computational time in SVM mutil-class algorithms. Model selection for SVM involving kernel and the margin parameter values selection which is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyper-spectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, particle swarm algorithm is introduced to the optimal selection of SVM (PSSVM) kernel parameter σ and margin parameter C to improve the modelling efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for evaluating the novel PSSVM, as well as traditional SVM classifier with general Grid-Search cross-validation method (GSSVM). And then, evaluation indexes including SVM model training time, classification Overall Accuracy (OA) and Kappa index of both PSSVM and GSSVM are all analyzed quantitatively. It is demonstrated that OA of PSSVM on test samples and whole image are 85% and 82%, the differences with that of GSSVM are both within 0.08% respectively. And Kappa indexes reach 0.82 and 0.77, the differences with that of GSSVM are both within 0.001. While the modelling time of PSSVM can be only 1/10 of that of GSSVM, and the modelling. Therefore, PSSVM is an fast and accurate algorithm for hyper-spectral image classification and is superior to GSSVM
Full Text Available An effective approach based on the Minimum Spanning Forest (MSF, grown from automatically selected markers using Support Vector Machines (SVM, has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.
Chu, Bingquan; Yu, Keqiang; Zhao, Yanru
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
Chu, Bingquan; Yu, Keqiang; Zhao, Yanru; He, Yong
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.
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.
Liu, Qing-Jie; Jing, Lin-Hai; Wang, Meng-Fei; Lin, Qi-Zhong
Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.
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.
Ashokkumar, L.; Shanmugam, S.
Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral
Full Text Available Cooking loss (CL is a critical quality attribute directly relating to meat juiciness. The potential of the hyperspectral imaging (HSI technique was investigated for non-invasively classifying and visualizing the CL of fresh broiler breast meat. Hyperspectral images of total 75 fresh broiler breast fillets were acquired by the system operating in the visible and near-infrared (VNIR, 400–1000 nm range. Mean spectra were extracted from regions of interest (ROIs determined by pure muscle tissue pixels. CL was firstly measured by calculating the weight loss in cooking, and then fillets were grouped into high-CL and low-CL according to the threshold of 20%. The classification methods partial least square-discriminant analysis (PLS-DA and radial basis function-support vector machine (RBF-SVM were applied, respectively, to determine the optimal spectral calibration strategy. Results showed that the PLS-DA model developed using the data, that is, first-order derivative (Der1 of VNIR full spectra, performed best with correct classification rates (CCRs of 0.90 and 0.79 for the calibration and prediction sets, respectively. Furthermore, to simplify the optimal PLS-DA model and make it practical, effective wavelengths were individually selected using uninformative variable elimination (UVE and competitive adaptive reweighted sampling (CARS. Through performance comparison, the CARS-PLS-DA combination was identified as the optimal method and the PLS-DA model built with 18 informative wavelengths selected by CARS resulted in good CCRs of 0.86 and 0.79. Finally, classification maps were created by predicting CL categories of each pixel in the VNIR hyperspectral images using the CARS-PLS-DA model, and the general CL categories of fillets were readily discernible. The overall results were encouraging and showed the promising potential of the VNIR HSI technique for classifying fresh broiler breast fillets into different CL categories.
Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang
In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.
Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.
Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.
Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.
Zhang, Mengmeng; Li, Wei; Du, Qian
Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.
Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu
Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.
Wan, Xiaoqing; Zhao, Chunhui
As a competitive machine learning algorithm, the stacked sparse autoencoder (SSA) has achieved outstanding popularity in exploiting high-level features for classification of hyperspectral images (HSIs). In general, in the SSA architecture, the nodes between adjacent layers are fully connected and need to be iteratively fine-tuned during the pretraining stage; however, the nodes of previous layers further away may be less likely to have a dense correlation to the given node of subsequent layers. Therefore, to reduce the classification error and increase the learning rate, this paper proposes the general framework of locally connected SSA; that is, the biologically inspired local receptive field (LRF) constrained SSA architecture is employed to simultaneously characterize the local correlations of spectral features and extract high-level feature representations of hyperspectral data. In addition, the appropriate receptive field constraint is concurrently updated by measuring the spatial distances from the neighbor nodes to the corresponding node. Finally, the efficient random forest classifier is cascaded to the last hidden layer of the SSA architecture as a benchmark classifier. Experimental results on two real HSI datasets demonstrate that the proposed hierarchical LRF constrained stacked sparse autoencoder and random forest (SSARF) provides encouraging results with respect to other contrastive methods, for instance, the improvements of overall accuracy in a range of 0.72%-10.87% for the Indian Pines dataset and 0.74%-7.90% for the Kennedy Space Center dataset; moreover, it generates lower running time compared with the result provided by similar SSARF based methodology.
Sinclair, Michael B.; Jones, Howland D. T.
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.
Amigo Rubio, Jose Manuel; Babamoradi, Hamid; Elcoroaristizabal Martin, Saioa
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...
Palmieri, Roberta; Bonifazi, Giuseppe; Serranti, Silvia
The recovery of materials from Demolition Waste (DW) represents one of the main target of the recycling industry and the its characterization is important in order to set up efficient sorting and/or quality control systems. End-Of-Life (EOL) concrete materials identification is necessary to maximize DW conversion into useful secondary raw materials, so it is fundamental to develop strategies for the implementation of an automatic recognition system of the recovered products. In this paper, HyperSpectral Imaging (HSI) technique was applied in order to detect DW composition. Hyperspectral images were acquired by a laboratory device equipped with a HSI sensing device working in the near infrared range (1000-1700 nm): NIR Spectral Camera™, embedding an ImSpector™ N17E (SPECIM Ltd, Finland). Acquired spectral data were analyzed adopting the PLS_Toolbox (Version 7.5, Eigenvector Research, Inc.) under Matlab® environment (Version 7.11.1, The Mathworks, Inc.), applying different chemometric methods: Principal Component Analysis (PCA) for exploratory data approach and Partial Least Square- Discriminant Analysis (PLS-DA) to build classification models. Results showed that it is possible to recognize DW materials, distinguishing recycled aggregates from contaminants (e.g. bricks, gypsum, plastics, wood, foam, etc.). The developed procedure is cheap, fast and non-destructive: it could be used to make some steps of the recycling process more efficient and less expensive.
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...
Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.
Full Text Available Hyperspectral images (HSI provide rich information which may not be captured by other sensing technologies and therefore gradually find a wide range of applications. However, they also generate a large amount of irrelevant or redundant data for a specific task. This causes a number of issues including significantly increased computation time, complexity and scale of prediction models mapping the data to semantics (e.g., classification, and the need of a large amount of labelled data for training. Particularly, it is generally difficult and expensive for experts to acquire sufficient training samples in many applications. This paper addresses these issues by exploring a number of classical dimension reduction algorithms in machine learning communities for HSI classification. To reduce the size of training dataset, feature selection (e.g., mutual information, minimal redundancy maximal relevance and feature extraction (e.g., Principal Component Analysis (PCA, Kernel PCA are adopted to augment a baseline classification method, Support Vector Machine (SVM. The proposed algorithms are evaluated using a real HSI dataset. It is shown that PCA yields the most promising performance in reducing the number of features or spectral bands. It is observed that while significantly reducing the computational complexity, the proposed method can achieve better classification results over the classic SVM on a small training dataset, which makes it suitable for real-time applications or when only limited training data are available. Furthermore, it can also achieve performances similar to the classic SVM on large datasets but with much less computing time.
Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that
Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa
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.
Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen
Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Swanson, Robin J.
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.
Swanson, Robin J.
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.
Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo
Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples
Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.
Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan
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. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.
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.
Khouj, Yasser; Dawson, Jeremy; Coad, James; Vona-Davis, Linda
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.
Wei, Xiaohui; Zhu, Wen; Liao, Bo; Gu, Changlong; Li, Weibiao
The key question of sparse coding (SC) is how to exploit the information that already exists to acquire the robust sparse representations (SRs) of distinguishing different objects for hyperspectral image (HSI) classification. We propose a multi-information fusion SC framework, which fuses the spectral, spatial, and label information in the same level, to solve the above question. In particular, pixels from disjointed spatial clusters, which are obtained by cutting the given HSI in space, are individually and sparsely encoded. Then, due to the importance of spatial structure, graph- and hypergraph-based regularizers are enforced to motivate the obtained representations smoothness and to preserve the local consistency for each spatial cluster. The latter simultaneously considers the spectrum, spatial, and label information of multiple pixels that have a great probability with the same label. Finally, a linear support vector machine is selected as the final classifier with the learned SRs as input. Experiments conducted on three frequently used real HSIs show that our methods can achieve satisfactory results compared with other state-of-the-art methods.
Optical method with hyperspectral microscope imaging (HMI) has potential for identification of foodborne pathogenic bacteria from microcolonies rapidly with a cell level. A HMI system that provides both spatial and spectral information could be an effective tool for analyzing spectral characteristic...
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.
Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan
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.
Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.
Bhardwaj, Kaushal; Patra, Swarnajyoti
Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.
Full Text Available In this paper, we introduce a novel classification framework for hyperspectral images (HSIs by jointly employing spectral, spatial, and hierarchical structure information. In this framework, the three types of information are integrated into the SVM classifier in a way of multiple kernels. Specifically, the spectral kernel is constructed through each pixel’s vector value in the original HSI, and the spatial kernel is modeled by using the extended morphological profile method due to its simplicity and effectiveness. To accurately characterize hierarchical structure features, the techniques of Fish-Markov selector (FMS, marker-based hierarchical segmentation (MHSEG and algebraic multigrid (AMG are combined. First, the FMS algorithm is used on the original HSI for feature selection to produce its spectral subset. Then, the multigrid structure of this subset is constructed using the AMG method. Subsequently, the MHSEG algorithm is exploited to obtain a hierarchy consist of a series of segmentation maps. Finally, the hierarchical structure information is represented by using these segmentation maps. The main contributions of this work is to present an effective composite kernel for HSI classification by utilizing spatial structure information in multiple scales. Experiments were conducted on two hyperspectral remote sensing images to validate that the proposed framework can achieve better classification results than several popular kernel-based classification methods in terms of both qualitative and quantitative analysis. Specifically, the proposed classification framework can achieve 13.46–15.61% in average higher than the standard SVM classifier under different training sets in the terms of overall accuracy.
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.
Truitt, Paul W.; Soliz, Peter; Meigs, Andrew D.; Otten, Leonard John, III
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.
Kehlet, Louis Martinus; Tidemand-Lichtenberg, Peter; Dam, Jeppe Seidelin
In this Letter, hyperspectral imaging in the mid-IR spectral region is demonstrated based on nonlinear frequency upconversion and subsequent imaging using a standard Si-based CCD camera. A series of upconverted images are acquired with different phase match conditions for the nonlinear frequency...... conversion process. From this, a sequence of monochromatic images in the 3.2-3.4 mu m range is generated. The imaged object consists of a standard United States Air Force resolution target combined with a polystyrene film, resulting in the presence of both spatial and spectral information in the infrared...... image. (C) 2015 Optical Society of America...
Mao, Chengye; Smith, David; Lanoue, Mark A.; Poole, Gavin H.; Heitschmidt, Jerry; Martinez, Luis; Windham, William A.; Lawrence, Kurt C.; Park, Bosoon
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.
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.
Full Text Available Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF, in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN. The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.
Richsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.
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.
The objective of this study was to discriminate by on-line hyperspectral imaging, taxonomic plant families comprised of different grassland species. Plants were collected from semi-natural meadows of the National Apuseni Park, Apuseni Mountains, Gârda area (Romania) according to botanical families. ...
Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.
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.
Lu, Guolan; Fei, Baowei
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
Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.
Salmonella is a major cause of foodborne disease outbreaks resulting from the consumption of contaminated food products in the United States. This paper reports the development of a hyperspectral imaging technique for detecting and differentiating two of the most common Salmonella serotypes, Salmonella Enteritidis (SE) and Salmonella Typhimurium (ST), from background microflora that are often found in poultry carcass rinse. Presumptive positive screening of colonies with a traditional direct plating method is a labor intensive and time consuming task. Thus, this paper is concerned with the detection of differences in spectral characteristics among the pure SE, ST, and background microflora grown on brilliant green sulfa (BGS) and xylose lysine tergitol 4 (XLT4) agar media with a spread plating technique. Visible near-infrared hyperspectral imaging, providing the spectral and spatial information unique to each microorganism, was utilized to differentiate SE and ST from the background microflora. A total of 10 classification models, including five machine learning algorithms, each without and with principal component analysis (PCA), were validated and compared to find the best model in classification accuracy. The five machine learning (classification) algorithms used in this study were Mahalanobis distance (MD), k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). The average classification accuracy of all 10 models on a calibration (or training) set of the pure cultures on BGS agar plates was 98% (Kappa coefficient = 0.95) in determining the presence of SE and/or ST although it was difficult to differentiate between SE and ST. The average classification accuracy of all 10 models on a training set for ST detection on XLT4 agar was over 99% (Kappa coefficient = 0.99) although SE colonies on XLT4 agar were difficult to differentiate from background microflora. The average classification
Sun, Dandan; Liu, Fang; Gao, Jiaobo; Sun, Kefeng; Hu, Yu; Li, Yu; Xie, Junhu; Zhang, Lei
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.
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...
Yu, Hui; Zhang, Zhi-jie; Lei, Bo; Wang, Chen-sheng
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.
Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.
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.
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.
Full Text Available The diverse density (DD algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels. However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor and the Push-broom Hyperspectral Imager (PHI are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively.
Dijkstra, Klaas; van de Loosdrecht, Jaap; Schomaker, Lambert; Wiering, Marco
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...
Full Text Available Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77 than one using the full set of wavebands (77%, Kappa statistic value = 0.65. Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages.
Leavesley, Silas J.; Deal, Joshua; Hill, Shante; Martin, Will A.; Lall, Malvika; Lopez, Carmen; Rider, Paul F.; Rich, Thomas C.; Boudreaux, Carole W.
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.
Tian, Lixun; Liao, Ningfang; Chai, Ali
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.
Full Text Available Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier—MLC, machine learning algorithms (support vector machine—SVM, random forest—RF and feature extraction (minimum noise fraction (MNF-transformation on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400–1000 nm, a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2–15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%, but the use of a smaller training dataset (10 training pixels significantly reduced the accuracy of classification (52.56%. Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC
Serranti, Silvia; Gargiulo, Aldo; Bonifazi, Giuseppe
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.
Richardson, Laurie L.
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.
Gao, Lianru; Yu, Haoyang; Zhang, Bing; Li, Qingting
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.
Ortega, Samuel; Fabelo, Himar; Camacho, Rafael; de la Luz Plaza, María; Callicó, Gustavo M; Sarmiento, Roberto
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.
Cheng, L-J.; Mahoney, J.; Reyes, F.; Suiter, H.
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.
Akhtar, Naveed; Mian, Ajmal
We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.
Li, Wei; Wu, Lucheng; Qiu, Xianbo; Ran, Qiong; Xie, Xiaoming
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)
Serranti, S.; Bonifazi, G.
Hyperspectral imaging (HSI) is an emerging technique that combines the imaging properties of a digital camera with the spectroscopic properties of a spectrometer able to detect the spectral attributes of each pixel in an image. For these characteristics, HSI allows to qualitatively and quantitatively evaluate the effects of the interactions of light with organic and/or inorganic materials. The results of this interaction are usually displayed as a spectral signature characterized by a sequence of energy values, in a pre-defined wavelength interval, for each of the investigated/collected wavelength. Following this approach, it is thus possible to collect, in a fast and reliable way, spectral information that are strictly linked to chemical-physical characteristics of the investigated materials and/or products. Considering that in an hyperspectral image the spectrum of each pixel can be analyzed, HSI can be considered as one of the best nondestructive technology allowing to perform the most accurate and detailed information extraction. HSI can be applied in different wavelength fields, the most common are the visible (VIS: 400-700 nm), the near infrared (NIR: 1000-1700 nm) and the short wave infrared (SWIR: 1000-2500 nm). It can be applied for inspections from micro- to macro-scale, up to remote sensing. HSI produces a large amount of information due to the great number of continuous collected spectral bands. Such an approach, when successful, is quite challenging being usually reliable, robust and characterized by lower costs, if compared with those usually associated to commonly applied analytical off-line and/or on-line analytical approaches. More and more applications have been thus developed and tested, in these last years, especially in food inspection, with a large range of investigated products, such as fruits and vegetables, meat, fish, eggs and cereals, but also in medicine and pharmaceutical sector, in cultural heritage, in material characterization and in
Liu, Da; Li, Jianxun
Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.
Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira
We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.
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...
Bonifazi, Giuseppe; Capobianco, Giuseppe; Serranti, Silvia
The aim of this work was to recognize different polymer flakes from mixed plastic waste through an innovative hierarchical classification strategy based on hyperspectral imaging, with particular reference to low density polyethylene (LDPE) and high-density polyethylene (HDPE). A plastic waste composition assessment, including also LDPE and HDPE identification, may help to define optimal recycling strategies for product quality control. Correct handling of plastic waste is essential for its further "sustainable" recovery, maximizing the sorting performance in particular for plastics with similar characteristics as LDPE and HDPE. Five different plastic waste samples were chosen for the investigation: polypropylene (PP), LDPE, HDPE, polystyrene (PS) and polyvinyl chloride (PVC). A calibration dataset was realized utilizing the corresponding virgin polymers. Hyperspectral imaging in the short-wave infrared range (1000-2500 nm) was thus applied to evaluate the different plastic spectral attributes finalized to perform their recognition/classification. After exploring polymer spectral differences by principal component analysis (PCA), a hierarchical partial least squares discriminant analysis (PLS-DA) model was built allowing the five different polymers to be recognized. The proposed methodology, based on hierarchical classification, is very powerful and fast, allowing to recognize the five different polymers in a single step.
Taher, Akar; Chehdi, Kacem; Cariou, Claude
In this paper a new unsupervised nonparametric cooperative and adaptive hyperspectral image segmentation approach is presented. The hyperspectral images are partitioned band by band in parallel and intermediate classification results are evaluated and fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are used in parallel cooperation, namely the Fuzzy C-means (FCM) method, and the Linde-Buzo-Gray (LBG) algorithm, to segment each band of the image. The originality of the approach relies firstly on its local adaptation to the type of regions in an image (textured, non-textured), and secondly on the introduction of several levels of evaluation and validation of intermediate segmentation results before obtaining the final partitioning of the image. For the management of similar or conflicting results issued from the two classification methods, we gradually introduced various assessment steps that exploit the information of each spectral band and its adjacent bands, and finally the information of all the spectral bands. In our approach, the detected textured and non-textured regions are treated separately from feature extraction step, up to the final classification results. This approach was first evaluated on a large number of monocomponent images constructed from the Brodatz album. Then it was evaluated on two real applications using a respectively multispectral image for Cedar trees detection in the region of Baabdat (Lebanon) and a hyperspectral image for identification of invasive and non invasive vegetation in the region of Cieza (Spain). A correct classification rate (CCR) for the first application is over 97% and for the second application the average correct classification rate (ACCR) is over 99%.
Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves
This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.
Mo, Changyeun; Kim, Giyoung; Lim, Jongguk; Kim, Moon S; Cho, Hyunjeong; Cho, Byoung-Kwan
Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.
.... 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...
Mohd Hasmadi, I.; Kamaruzaman, J.
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.
Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.
A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or ``hyperspectral`` imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne`s Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image ``texture spectra`` derived from fractal signatures computed for subimage tiles at each wavelength.
Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.
A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or hyperspectral'' imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne's Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image texture spectra'' derived from fractal signatures computed for subimage tiles at each wavelength.
Guerra, Raúl; Melián, José; López, Sebastián.; Sarmiento, Roberto
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.
Larsen, Rasmus; Nielsen, Allan Aasbjerg; Arngren, Morten
In hyperspectral image analysis an exploratory approach to analyse the image data is to conduct subspace projections. As linear projections often fail to capture the underlying structure of the data, we present kernel based subspace projections of PCA and Maximum Autocorrelation Factors (MAF...
Denstedt, Martin; Pukstad, Brita S.; Paluchowski, Lukasz A.; Hernandez-Palacios, Julio E.; Randeberg, Lise L.
The healing process of chronic wounds is complex, and the complete pathogenesis is not known. Diagnosis is currently based on visual inspection, biopsies and collection of samples from the wound surface. This is often time consuming, expensive and to some extent subjective procedures. Hyperspectral imaging has been shown to be a promising modality for optical diagnostics. The main objective of this study was to identify a suitable technique for reproducible classification of hyperspectral data from a wound and the surrounding tissue. Two statistical classification methods have been tested and compared to the performance of a dermatologist. Hyperspectral images (400-1000 nm) were collected from patients with venous leg ulcers using a pushbroom-scanning camera (VNIR 1600, Norsk Elektro Optikk AS).Wounds were examined regularly over 4 - 6 weeks. The patients were evaluated by a dermatologist at every appointment. One patient has been selected for presentation in this paper (female, age 53 years). The oxygen saturation of the wound area was determined by wavelength ratio metrics. Spectral angle mapping (SAM) and k-means clustering were used for classification. Automatic extraction of endmember spectra was employed to minimize human interaction. A comparison of the methods shows that k-means clustering is the most stable method over time, and shows the best overlap with the dermatologist's assessment of the wound border. The results are assumed to be affected by the data preprocessing and chosen endmember extraction algorithm. Results indicate that it is possible to develop an automated method for reliable classification of wounds based on hyperspectral data.
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.
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
Perumal, K.; Bhaskaran, R.
Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the ...
Gualtieri, J. Anthony; Cromp, R. F.
The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.
Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan
Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.
Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G.
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.
Deal, Joshua; Harris, Bradley; Martin, Will; Lall, Malvika; Lopez, Carmen; Rider, Paul; Boudreaux, Carole; Rich, Thomas; Leavesley, Silas J.
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.
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.
Puttonen, Eetu; Jaakkola, Anttoni; Litkey, Paula; Hyyppä, Juha
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
Leavesley, Silas; Jiang, Yanan; Patsekin, Valery; Hall, Heidi; Vizard, Douglas; Robinson, J. Paul
Molecular imaging is a rapidly growing area of research, fueled by needs in pharmaceutical drug-development for methods for high-throughput screening, pre-clinical and clinical screening for visualizing tumor growth and drug targeting, and a growing number of applications in the molecular biology fields. Small animal fluorescence imaging employs fluorescent probes to target molecular events in vivo, with a large number of molecular targeting probes readily available. The ease at which new targeting compounds can be developed, the short acquisition times, and the low cost (compared to microCT, MRI, or PET) makes fluorescence imaging attractive. However, small animal fluorescence imaging suffers from high optical scattering, absorption, and autofluorescence. Much of these problems can be overcome through multispectral imaging techniques, which collect images at different fluorescence emission wavelengths, followed by analysis, classification, and spectral deconvolution methods to isolate signals from fluorescence emission. We present an alternative to the current method, using hyperspectral excitation scanning (spectral selection imaging), a technique that allows excitation at any wavelength in the visible and near-infrared wavelength range. In many cases, excitation imaging may be more effective at identifying specific fluorescence signals because of the higher complexity of the fluorophore excitation spectrum. Because the excitation is filtered and not the emission, the resolution limit and image shift imposed by acousto-optic tunable filters have no effect on imager performance. We will discuss design of the imager, optimizing the imager for use in small animal fluorescence imaging, and application of spectral analysis and classification methods for identifying specific fluorescence signals.
Fields, D. [Lawrence Livermore National Lab., CA (United States); Bennett, C.; Carter, M.
LIFTIRS, the Livermore Imaging Fourier Transform InfraRed Spectrometer, recently developed at LLNL, is an instrument which enables extremely efficient collection and analysis of hyperspectral imaging data. LIFTIRS produces a spatial format of 128x128 pixels, with spectral resolution arbitrarily variable up to a maximum of 0.25 inverse centimeters. Time resolution and spectral resolution can be traded off for each other with great flexibility. We will discuss recent measurements made with this instrument, and present typical images and spectra.
Liang, Zhang; Lianru, Gao; Bing, Zhang
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
National Aeronautics and Space Administration — The Gimbal-stabilized Compact Hyperspectral Imaging System (GCHIS) fully integrates multi-sensor spectral imaging, stereovision, GPS and inertial measurement,...
Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel
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
Wendel, Alexander; Underwood, James
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
Full Text Available Extended morphological profile (EMP is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.
Previous research has demonstrated an optical method with acousto-optic tunable filter (AOTF) based hyperspectral microscope imaging (HMI) had potential for classifying gram-negative from gram-positive foodborne pathogenic bacteria rapidly and nondestructively with a minimum sample preparation. In t...
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.
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.
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...
Mo, Changyeun; Kim, Giyoung; Lim, Jongguk
The consumption of fresh-cut agricultural produce in Korea has been growing. The browning of fresh-cut vegetables that occurs during storage and foreign substances such as worms and slugs are some of the main causes of consumers' concerns with respect to safety and hygiene. The purpose of this study is to develop an on-line system for evaluating quality of agricultural products using hyperspectral imaging technology. The online evaluation system with single visible-near infrared hyperspectral camera in the range of 400 nm to 1000 nm that can assess quality of both surfaces of agricultural products such as fresh-cut lettuce was designed. Algorithms to detect browning surface were developed for this system. The optimal wavebands for discriminating between browning and sound lettuce as well as between browning lettuce and the conveyor belt were investigated using the correlation analysis and the one-way analysis of variance method. The imaging algorithms to discriminate the browning lettuces were developed using the optimal wavebands. The ratio image (RI) algorithm of the 533 nm and 697 nm images (RI533/697) for abaxial surface lettuce and the ratio image algorithm (RI533/697) and subtraction image (SI) algorithm (SI538-697) for adaxial surface lettuce had the highest classification accuracies. The classification accuracy of browning and sound lettuce was 100.0% and above 96.0%, respectively, for the both surfaces. The overall results show that the online hyperspectral imaging system could potentially be used to assess quality of agricultural products.
Yoon, Seung-Chul; Shin, Tae-Sung; Heitschmidt, Gerald W.; Lawrence, Kurt C.; Park, Bosoon; Gamble, Gary
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
Ramirez, Andres; Rahnemoonfar, Maryam
A hyperspectral image provides multidimensional figure rich in data consisting of hundreds of spectral dimensions. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research presents a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyzing a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low-level parallel programming models. Additionally, Hadoop was used as an open-source version of the MapReduce parallel programming model. This research compared classification accuracy results and timing results between the Hadoop and GPU system and tested it against the following test cases: the CPU and GPU test case, a CPU test case and a test case where no dimensional reduction was applied.
Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar
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.
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...
Richtsmeier, Steven C.; Singer-Berk, Alexander; Bernstein, Lawrence S.
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.
Full Text Available The existence of various natural objects such as grass, trees, and rivers along with artificial manmade features such as buildings and roads, make it difficult to classify ground objects. Consequently using single data or simple classification approach cannot improve classification results in object identification. Also, using of a variety of data from different sensors; increase the accuracy of spatial and spectral information. In this paper, we proposed a classification algorithm on joint use of hyperspectral and Lidar (Light Detection and Ranging data based on dimension reduction. First, some feature extraction techniques are applied to achieve more information from Lidar and hyperspectral data. Also Principal component analysis (PCA and Minimum Noise Fraction (MNF have been utilized to reduce the dimension of spectral features. The number of 30 features containing the most information of the hyperspectral images is considered for both PCA and MNF. In addition, Normalized Difference Vegetation Index (NDVI has been measured to highlight the vegetation. Furthermore, the extracted features from Lidar data calculated based on relation between every pixel of data and surrounding pixels in local neighbourhood windows. The extracted features are based on the Grey Level Co-occurrence Matrix (GLCM matrix. In second step, classification is operated in all features which obtained by MNF, PCA, NDVI and GLCM and trained by class samples. After this step, two classification maps are obtained by SVM classifier with MNF+NDVI+GLCM features and PCA+NDVI+GLCM features, respectively. Finally, the classified images are fused together to create final classification map by decision fusion based majority voting strategy.
Zhang, Mo; Vozel, Benoit; Chehdi, Kacem; Uss, Mykhail; Abramov, Sergey; Lukin, Vladimir
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
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.
Liu, L.; Xu, L.; Peng, J.
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.
Bonifazi, Giuseppe; Picone, Nicoletta; Serranti, Silvia
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.
Aranki, Nazeeh; Namkung, Jeffrey; Villapando, Carlos; Kiely, Aaron; Klimesh, Matthew; Xie, Hua
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
Yuan, X.; Zhang, D.; Wang, Ch.; Dai, B.; Zhao, M.; Li, B.
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.
Hinnrichs, Michele; Hinnrichs, Bradford; McCutchen, Earl
Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera, both MWIR and LWIR, small enough to serve as a payload on a miniature unmanned aerial vehicles. The optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of the sensor. This new and innovative approach to infrared hyperspectral imaging spectrometer uses micro-optics and will be explained in this paper. The micro-optics are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a mini-UAV or commercial quadcopter. Also, an example of how this technology can easily be used to quantify a hydrocarbon gas leak's volume and mass flowrates. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the spatial resolution. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4
Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang-Hee; Cho, Byoung-Kwan
Corn is one of the most cultivated crops all over world as food for humans as well as animals. Optimized agronomic practices and improved technological interventions during planting, harvesting and post-harvest handling are critical to improving the quantity and quality of corn production. Seed germination and vigor are the primary determinants of high yield notwithstanding any other factors that may play during the growth period. Seed viability may be lost during storage due to unfavorable conditions e.g. moisture content and temperatures, or physical damage during mechanical processing e.g. shelling, or over heating during drying. It is therefore vital for seed companies and farmers to test and ascertain seed viability to avoid losses of any kind. This study aimed at investigating the possibility of using hyperspectral imaging (HSI) technique to discriminate viable and nonviable corn seeds. A group of corn samples were heat treated by using microwave process while a group of seeds were kept as control group (untreated). The hyperspectral images of corn seeds of both groups were captured between 400 and 2500 nm wave range. Partial least squares discriminant analysis (PLS-DA) was built for the classification of aged (heat treated) and normal (untreated) corn seeds. The model showed highest classification accuracy of 97.6% (calibration) and 95.6% (prediction) in the SWIR region of the HSI. Furthermore, the PLS-DA and binary images were capable to provide the visual information of treated and untreated corn seeds. The overall results suggest that HSI technique is accurate for classification of viable and non-viable seeds with non-destructive manner.
Edelman, G. J.; Gaston, E.; van Leeuwen, T. G.; Cullen, P. J.; Aalders, M. C. G.
Hyperspectral imaging (HSI) integrates conventional imaging and spectroscopy, to obtain both spatial and spectral information from a specimen. This technique enables investigators to analyze the chemical composition of traces and simultaneously visualize their spatial distribution. HSI offers
Wang, Zi; Zheng, Wei; Jian, Lin; Huang, Zhiwei
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.
Santos, Bruno O.; Valença, Jonatas; Júlio, Eduardo
All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly
Li, Cuiling; Wang, Xiu; Zhao, Xueguan; Meng, Zhijun; Zou, Wei
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.
Gowen, Aoife A; Feng, Yaoze; Gaston, Edurne; Valdramidis, Vasilis
Hyperspectral chemical imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence and near-infrared chemical imaging). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods). In tandem with recent developments in instrumentation and sampling protocols, applications of HSI in microbiology have increased rapidly. This article gives a brief overview of the fundamentals of HSI and a comprehensive review of applications of HSI in microbiology over the past 10 years. Technical challenges and future perspectives for these techniques are also discussed. Copyright © 2015 Elsevier B.V. All rights reserved.
Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca
We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.
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.
Makki, Ihab; Younes, Rafic; Francis, Clovis; Bianchi, Tiziano; Zucchetti, Massimo
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.
J. Bruce Rafert
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.
Tidemand-Lichtenberg, Peter; Kehlet, Louis Martinus; Sanders, Nicolai Højer
Different schemes for upconversion mid-IR hyperspectral imaging is implemented and compared in terms of spectral coverage, spectral resolution, speed and noise. Phasematch scanning and scanning of the object within the field of view is considered....
Liu, F.; Seinstra, F.J.; Plaza, A.J.
Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of
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.
Full Text Available Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.
Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun
Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.
Full Text Available Hyperspectral imaging (HSI is increasingly gaining acceptance in the medical field. Up until now, HSI has been used in conjunction with rigid endoscopy to detect cancer in vivo. The logical next step is to pair HSI with flexible endoscopy, since it improves access to hard-to-reach areas. While the flexible endoscope’s fiber optic cables provide the advantage of flexibility, they also introduce an interfering honeycomb-like pattern onto images. Due to the substantial impact this pattern has on locating cancerous tissue, it must be removed before the HS data can be further processed. Thereby, the loss of information is to minimize avoiding the suppression of small-area variations of pixel values. We have developed a system that uses flexible endoscopy to record HS cubes of the larynx and designed a special filtering technique to remove the honeycomb-like pattern with minimal loss of information. We have confirmed its feasibility by comparing it to conventional filtering techniques using an objective metric and by applying unsupervised and supervised classifications to raw and pre-processed HS cubes. Compared to conventional techniques, our method successfully removes the honeycomb-like pattern and considerably improves classification performance, while preserving image details.
Improved scanners to be incorporated into hyperspectral microscope-based imaging systems have been invented. Heretofore, in microscopic imaging, including spectral imaging, it has been customary to either move the specimen relative to the optical assembly that includes the microscope or else move the entire assembly relative to the specimen. It becomes extremely difficult to control such scanning when submicron translation increments are required, because the high magnification of the microscope enlarges all movements in the specimen image on the focal plane. To overcome this difficulty, in a system based on this invention, no attempt would be made to move either the specimen or the optical assembly. Instead, an objective lens would be moved within the assembly so as to cause translation of the image at the focal plane: the effect would be equivalent to scanning in the focal plane. The upper part of the figure depicts a generic proposed microscope-based hyperspectral imaging system incorporating the invention. The optical assembly of this system would include an objective lens (normally, a microscope objective lens) and a charge-coupled-device (CCD) camera. The objective lens would be mounted on a servomotor-driven translation stage, which would be capable of moving the lens in precisely controlled increments, relative to the camera, parallel to the focal-plane scan axis. The output of the CCD camera would be digitized and fed to a frame grabber in a computer. The computer would store the frame-grabber output for subsequent viewing and/or processing of images. The computer would contain a position-control interface board, through which it would control the servomotor. There are several versions of the invention. An essential feature common to all versions is that the stationary optical subassembly containing the camera would also contain a spatial window, at the focal plane of the objective lens, that would pass only a selected portion of the image. In one version
Clancy, Neil T.; Elson, Daniel S.; Teare, Julian
Standard endoscopic tools restrict clinicians to making subjective visual assessments of lesions detected in the bowel, with classification results depending strongly on experience level and training. Histological examination of resected tissue remains the diagnostic gold standard, meaning that all detected lesions are routinely removed. This subjects the patient to risk of polypectomy-related injury, and places significant workload and economic burdens on the hospital. An objective endoscopic classification method would allow hyperplastic polyps, with no malignant potential, to be left in situ, or low grade adenomas to be resected and discarded without histology. A miniature multimodal flexible endoscope is proposed to obtain hyperspectral reflectance and dual excitation autofluorescence information from polyps in vivo. This is placed inside the working channel of a conventional colonoscope, with the external scanning and detection optics on a bedside trolley. A blue and violet laser diode pair excite endogenous fluorophores in the respiration chain, while the colonoscope's xenon light source provides broadband white light for diffuse reflectance measurements. A push-broom HSI scanner collects the hypercube. System characterisation experiments are presented, defining resolution limits as well as acquisition settings for optimal spectral, spatial and temporal performance. The first in vivo results in human subjects are presented, demonstrating the clinical utility of the device. The optical properties (reflectance and autofluorescence) of imaged polyps are quantified and compared to the histologically-confirmed tissue type as well as the clinician's visual assessment. Further clinical studies will allow construction of a full robust training dataset for development of classification schemes.
Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...
Schmalz, M.; Ritter, G.
Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination . Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra . Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs . However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and
Full Text Available Hyperspectral imaging (HSI allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min, making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.
Nubiato, Keni Eduardo Zanoni; Mazon, Madeline Rezende; Antonelo, Daniel Silva; Calkins, Chris R.; Naganathan, Govindarajan Konda; Subbiah, Jeyamkondan; da Luz e Silva, Saulo
The aim of this study was to evaluate the accuracy of classification of Nellore beef aged for 0, 7, 14, or 21 days and classification based on tenderness and aging period using a bench-top hyperspectral imaging system. A hyperspectral imaging system (λ = 928-2524 nm) was used to collect hyperspectral images of the Longissimus thoracis et lumborum (aging n = 376 and tenderness n = 345) of Nellore cattle. The image processing steps included selection of region of interest, extraction of spectra, and indentification and evalution of selected wavelengths for classification. Six linear discriminant models were developed to classify samples based on tenderness and aging period. The model using the first derivative of partial absorbance spectra (give wavelength range spectra) was able to classify steaks based on the tenderness with an overall accuracy of 89.8%. The model using the first derivative of full absorbance spectra was able to classify steaks based on aging period with an overall accuracy of 84.8%. The results demonstrate that the HIS may be a viable technology for classifying beef based on tenderness and aging period.
Lim, Hoong-Ta; Murukeshan, Vadakke Matham
The use of counterfeit banknotes increases crime rates and cripples the economy. New countermeasures are required to stop counterfeiters who use advancing technologies with criminal intent. Many countries started adopting polymer banknotes to replace paper notes, as polymer notes are more durable and have better quality. The research on authenticating such banknotes is of much interest to the forensic investigators. Hyperspectral imaging can be employed to build a spectral library of polymer notes, which can then be used for classification to authenticate these notes. This is however not widely reported and has become a research interest in forensic identification. This paper focuses on the use of hyperspectral imaging on polymer notes to build spectral libraries, using a pushbroom hyperspectral imager which has been previously reported. As an initial study, a spectral library will be built from three arbitrarily chosen regions of interest of five circulated genuine polymer notes. Principal component analysis is used for dimension reduction and to convert the information in the spectral library to principal components. A 99% confidence ellipse is formed around the cluster of principal component scores of each class and then used as classification criteria. The potential of the adopted methodology is demonstrated by the classification of the imaged regions as training samples.
Goodenough, David G.; Bannon, David
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
Honniball, Casey I.; Wright, Rob; Lucey, Paul G.
Hyperspectral imaging (HSI) in the Mid-Wave InfraRed (MWIR, 3-5 microns) can provide information on a variety of science applications from determining the chemical composition of lava lakes on Jupiter's moon Io, to investigating the amount of carbon liberated into the Earth's atmosphere during a wildfire. The limited signal available in the MWIR presents technical challenges to achieving high signal-to-noise ratios, and therefore it is typically necessary to cryogenically cool MWIR instruments. With recent improvements in microbolometer technology and emerging interferometric techniques, we have shown that uncooled microbolometers coupled with a Sagnac interferometer can achieve high signal-to-noise ratios for long-wave infrared HSI. To explore if this technique can be applied to the MWIR, this project, with funding from NASA, has built the Miniaturized Infrared Detector of Atmospheric Species (MIDAS). Standard characterization tests are used to compare MIDAS against a cryogenically cooled photon detector to evaluate the MIDAS instruments' ability to quantify gas concentrations. Atmospheric radiative transfer codes are in development to explore the limitations of MIDAS and identify the range of science objectives that MIDAS will most likely excel at. We will simulate science applications with gas cells filled with varying gas concentrations and varying source temperatures to verify our results from lab characterization and our atmospheric modeling code.
Full Text Available We investigated a lab-based hyperspectral imaging system’s response from pure (single and mixed (two algal cultures containing known algae types and volumetric combinations to characterize the system’s performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert’s law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements.
Carpentieri, Bruno; Pizzolante, Raffaele
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.
Jin, Senlin; Hui, Wangwei; Wang, Yunlong; Huang, Kaicheng; Shi, Qiushuai; Ying, Cuifeng; Liu, Dongqi; Ye, Qing; Zhou, Wenyuan; Tian, Jianguo
Hyperspectral imaging technology is playing an increasingly important role in the fields of food analysis, medicine and biotechnology. To improve the speed of operation and increase the light throughput in a compact equipment structure, a Fourier transform hyperspectral imaging system based on a single-pixel technique is proposed in this study. Compared with current imaging spectrometry approaches, the proposed system has a wider spectral range (400-1100 nm), a better spectral resolution (1 nm) and requires fewer measurement data (a sample rate of 6.25%). The performance of this system was verified by its application to the non-destructive testing of potatoes.
Lopatin, Javier; Fassnacht, Fabian E.; Kattenborn, Teja; Schmidtlein, Sebastian
Grasslands are one of the ecosystems that have been strongly intervened during the past decades due to anthropogenic impacts, affecting their structural and functional composition. To monitor the spatial and/or temporal changes of these environments, a reliable field survey is first needed. As quality relevés are usually expensive and time consuming, the amount of information available is usually poor or not well spatially distributed at the regional scale. In the present study, we investigate the possibility of a semi-automated method used for repeated surveys of monitoring sites. We analyze the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. The AISA+ imaging spectrometer mounted on a scaffold was applied to scan 1 m2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) the species number and structural diversity, (3) the species cover, and (4) the species functional types (bryophytes, forbs and graminoids). We found that the spatial resolution and the diversity level (mainly structural diversity) were the most important source of variation for the proposed approach. A spatial resolution below 1 cm produced relatively high model performances, while predictions with pixel sizes over that threshold produced non adequate results. Areas with low interspecies overlap reached classification median values of 0.8 (kappa). On the contrary, results were not satisfactory in plots with frequent interspecies overlap in multiple layers. By means of a bootstrapping procedure, we found that areas with shadows and mixed pixels introduce uncertainties into the classification. We conclude that the application of very high resolution hyperspectral remote sensing as a robust alternative or supplement to field surveys is possible for environments with low structural heterogeneity. This study presents the first try of a
Classification is extensively used in the context of medical image analysis for the purpose of diagnosis or prognosis. In order to classify image content correctly, one needs to extract efficient features with discriminative properties and build classifiers based on these features. In addition...... on characterizing human faces and emphysema disease in lung CT images....
Irby, Jon Trenton
Agricultural production has observed many changes in technology over the last 20 years. Producers are able to utilize technologies such as site-specific applicators and remotely sensed data to assist with decision making for best management practices which can improve crop production and provide protection to the environment. It is known that plant stress can interfere with photosynthetic reactions within the plant and/or the physical structure of the plant. Common types of stress associated with agricultural crops include herbicide induced stress, nutrient stress, and drought stress from lack of water. Herbicide induced crop stress is not a new problem. However, with increased acreage being planting in varieties/hybrids that contain herbicide resistant traits, herbicide injury to non-target crops will continue to be problematic for producers. With rapid adoption of herbicide-tolerant cropping systems, it is likely that herbicide induced stress will continue to be a major concern. To date, commercially available herbicide-tolerant varieties/hybrids contain traits which allow herbicides like glyphosate and glufosinate-ammonium to be applied as a broadcast application during the growing season. Both glyphosate and glufosinate-ammonium are broad spectrum herbicides which have activity on a large number of plant species, including major crops like non-transgenic soybean, corn, and cotton. Therefore, it is possible for crop stress from herbicide applications to occur in neighboring fields that contain susceptible crop varieties/hybrids. Nutrient and moisture stress as well as stress caused by herbicide applications can interact to influence yields in agricultural fields. If remotely sensed data can be used to accurately identify specific levels of crop stress, it is possible that producers can use this information to better assist them in crop management to maximize yields and protect their investments. This research was conducted to evaluate classification of specific
Bonifazi, Giuseppe; Serranti, Silvia
HyperSpectral Imaging (HSI) is based on the utilization of an integrated hardware and software (HW&SW) platform embedding conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Although HSI was originally developed for remote sensing, it has recently emerged as a powerful process analytical tool, for non-destructive analysis, in many research and industrial sectors. The possibility to apply on-line HSI based techniques in order to identify and quantify specific particulate solid systems characteristics is presented and critically evaluated. The originally developed HSI based logics can be profitably applied in order to develop fast, reliable and lowcost strategies for: i) quality control of particulate products that must comply with specific chemical, physical and biological constraints, ii) performance evaluation of manufacturing strategies related to processing chains and/or realtime tuning of operative variables and iii) classification-sorting actions addressed to recognize and separate different particulate solid products. Case studies, related to recent advances in the application of HSI to different industrial sectors, as agriculture, food, pharmaceuticals, solid waste handling and recycling, etc. and addressed to specific goals as contaminant detection, defect identification, constituent analysis and quality evaluation are described, according to authors' originally developed application.
Xu, Xiang; Lin, Feng
This book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one’s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical...
Full Text Available Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1 A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM derived from LiDAR data; 2 The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3 Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4 The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90 performed better than Li
Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.
Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79
Full Text Available Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging and CASI (Compact Airborne Spectrographic Imager hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity and CASI data (48 bands with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM and maximum likelihood (MLC classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions: (1 the layer stacking fusion data; (2 the PCA fusion data; (3 the LiDAR data alone; and (4 the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964 using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the
Weber, A.; Benecke, M.; Wendler, J.; Sieck, A.; Hübner, D.; Figgemeier, H.; Breiter, R.
AIM has developed SWIR modules including FPAs based on liquid phase epitaxy (LPE) grown MCT usable in a wide range of hyperspectral imaging applications. Silicon read-out integrated circuits (ROIC) provide various integration and readout modes including specific functions for spectral imaging applications. An important advantage of MCT based detectors is the tunable band gap. The spectral sensitivity of MCT detectors can be engineered to cover the extended SWIR spectral region up to 2.5μm without compromising in performance. AIM developed the technology to extend the spectral sensitivity of its SWIR modules also into the VIS. This has been successfully demonstrated for 384x288 and 1024x256 FPAs with 24μm pitch. Results are presented in this paper. The FPAs are integrated into compact dewar cooler configurations using different types of coolers, like rotary coolers, AIM's long life split linear cooler MCC030 or extreme long life SF100 Pulse Tube cooler. The SWIR modules include command and control electronics (CCE) which allow easy interfacing using a digital standard interface. The development status and performance results of AIM's latest MCT SWIR modules suitable for hyperspectral systems and applications will be presented.
Liu, Haijian; Wu, Changshan
Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.
Liu, Wanjun; Liang, Xuejian; Qu, Haicheng
Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.
Full Text Available Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set. Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.
Zrianina, Mariia; Kopf, Stephan
Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...
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...
Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano
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.
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
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.
Kong, Seong G.; Chen, Yud-Ren; Kim, Intaek; Kim, Moon S.
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.
Shan, Jiajia; Zhao, Junbo; Liu, Lifen; Zhang, Yituo; Wang, Xue; Wu, Fengchang
Hyperspectral imaging technology has been investigated as a possible way to detect microplastics contamination in soil directly and efficiently in this study. Hyperspectral images with wavelength range between 400 and 1000 nm were obtained from soil samples containing different materials including microplastics, fresh leaves, wilted leaves, rocks and dry branches. Supervised classification algorithms such as support vector machine (SVM), mahalanobis distance (MD) and maximum likelihood (ML) algorithms were used to identify microplastics from the other materials in hyperspectral images. To investigate the effect of particle size and color, white polyethylene (PE) and black PE particles extracted from soil with two different particle size ranges (1-5 mm and 0.5-1 mm) were studied in this work. The results showed that SVM was the most applicable method for detecting white PE in soil, with the precision of 84% and 77% for PE particles in size ranges of 1-5 mm and 0.5-1 mm respectively. The precision of black PE detection achieved by SVM were 58% and 76% for particles of 1-5 mm and 0.5-1 mm respectively. Six kinds of household polymers including drink bottle, bottle cap, rubber, packing bag, clothes hanger and plastic clip were used to validate the developed method, and the classification precision of polymers were obtained from 79% to 100% and 86%-99% for microplastics particle 1-5 mm and 0.5-1 mm respectively. The results indicate that hyperspectral imaging technology is a potential technique to determine and visualize the microplastics with particle size from 0.5 to 5 mm on soil surface directly. Copyright © 2018 Elsevier Ltd. All rights reserved.
Liu, Zhi; Møller, Flemming
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...
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the highdimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms
Kehlet, Louis Martinus; Sanders, Nicolai Højer; Tidemand-Lichtenberg, Peter
In this paper hyperspectral imaging in the mid-infrared wavelength region is realised using nonlinear frequency upconversion. The infrared light is converted to the near-infrared region for detection with a Si-based CCD camera. The object is translated in a predefined grid by motorized actuators...
The paper presents problems and solutions related to hyperspectral image pre-processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions. The proposed application and sample result of hyperspectral image analysis. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun
We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.
Full Text Available Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based, object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor
Walczykowski, P.; Jenerowicz, A.; Orych, A.; Siok, K.
Remote Sensing plays very important role in many different study fields, like hydrology, crop management, environmental and ecosystem studies. For all mentioned areas of interest different remote sensing and image processing techniques, such as: image classification (object and pixel- based), object identification, change detection, etc. can be applied. Most of this techniques use spectral reflectance coefficients as the basis for the identification and distinction of different objects and materials, e.g. monitoring of vegetation stress, identification of water pollutants, yield identification, etc. Spectral characteristics are usually acquired using discrete methods such as spectrometric measurements in both laboratory and field conditions. Such measurements however can be very time consuming, which has led many international researchers to investigate the reliability and accuracy of using image-based methods. According to published and ongoing studies, in order to acquire these spectral characteristics from images, it is necessary to have hyperspectral data. The presented article describes a series of experiments conducted using the push-broom Headwall MicroHyperspec A-series VNIR. This hyperspectral scanner allows for registration of images with more than 300 spectral channels with a 1.9 nm spectral bandwidth in the 380- 1000 nm range. The aim of these experiments was to establish a methodology for acquiring spectral reflectance characteristics of different forms of land cover using such sensor. All research work was conducted in controlled conditions from low altitudes. Hyperspectral images obtained with this specific type of sensor requires a unique approach in terms of post-processing, especially radiometric correction. Large amounts of acquired imagery data allowed the authors to establish a new post- processing approach. The developed methodology allowed the authors to obtain spectral reflectance coefficients from a hyperspectral sensor mounted on an
Erdal, I.; Sandvik Aas, L. M.; Cochrane, S.; Ekehaug, S.; Hansen, I. M.
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.
Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin
Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.
Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin
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.
A new method for calculation of latent variable space for exploratory analysis and dimension reduction of large hyperspectral images is proposed. The method is based on significant downsampling of image pixels with preservation of pixels’ structure in feature (variable) space. To achieve this, in...... can be used first of all for fast compression of large data arrays with principal component analysis or similar projection techniques....
Full Text Available The non-destructive testing of litchi fruit is of great significance to the fresh-keeping, storage and transportation of harvested litchis. To achieve quick and accurate micro-damage detection, a non-destructive grading test method for litchi fruits was studied using 400–1000 nm hyperspectral imaging technology. The Huaizhi litchi was chosen in this study, and the hyperspectral data average for the region of interest (ROI of litchi fruit was extracted for spectral data analysis. Then the hyperspectral data samples of fresh and micro-damaged litchi fruits were selected, and a partial least squares discriminant analysis (PLS-DA was used to establish a prediction model for the realization of qualitative analysis for litchis with different qualities. For the external validation set, the mean per-type recall and precision were 94.10% and 93.95%, respectively. Principal component analysis (PCA was used to determine the sensitive wavelength for recognition of litchi quality characteristics, with the results of wavelengths corresponding to the local extremum for the weight coefficient of PC3, i.e., 694, 725 and 798 nm. Then the single-band images corresponding to each sensitive wavelength were analyzed. Finally, the 7-dimension features of the PC3 image were extracted using the Gray Level Co-occurrence Matrix (GLCM. Through image processing, least squares support vector machine (LS-SVM modeling was conducted to classify the different qualities of litchis. The model was validated using the experiment data, and the average accuracy of the validation set was 93.75%, while the external validation set was 95%. The results indicate the feasibility of using hyperspectral imaging technology in litchi postpartum non-destructive detection and classification.
Li, Jiangbo; Rao, Xiuqin; Ying, Yibin
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.
Arrigoni, Simone; Turra, Giovanni; Signoroni, Alberto
With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available Hyperspectral imaging in the long-wave infrared (LWIR is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.
Václavík, Jan; Melich, Radek; Pintr, Pavel; Pleštil, Jan
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.
Full Text Available Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX hyperspectral images, registered in 288 spectral bands (10 September 2012. As the classification algorithm, Support Vector Machines (SVM was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA and sixteen out of twenty-four of user accuracy (UA. APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans.
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.
Kuula, J.; Puupponen, H.-H.; Rinta, H.; Pölönen, I.
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.
Dilip K. Prasad
Full Text Available We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds. The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS, moderate-resolution imaging spectroradiometer (MODIS, sea-viewing wide field-of-view sensor (SeaWiFS, coastal zone color scanner (CZCS, ocean and land colour instrument (OLCI, and visible infrared imaging radiometer suite (VIIRS sensors. Results are also shown for CIMEL’s SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.
Xie, Chuanqi; Li, Xiaoli; Shao, Yongni; He, Yong
This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.
Full Text Available This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb* and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR models. Competitive adaptive reweighted sampling (CARS and successive projections algorithm (SPA were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR] were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.
Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam
This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.
Ravi, Daniele; Fabelo, Himar; Callic, Gustavo Marrero; Yang, Guang-Zhong
Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing, and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumor classification map for intra-operative margin definition during brain surgery. However, existing approaches to dimensionality reduction based on manifold embedding can be time consuming and may not guarantee a consistent result, thus hindering final tissue classification. The proposed framework aims to overcome these problems through a process divided into two steps: dimensionality reduction based on an extension of the T-distributed stochastic neighbor approach is first performed and then a semantic segmentation technique is applied to the embedded results by using a Semantic Texton Forest for tissue classification. Detailed in vivo validation of the proposed method has been performed to demonstrate the potential clinical value of the system.
Schmitter, P.; Steinrücken, J.; Römer, C.; Ballvora, A.; Léon, J.; Rascher, U.; Plümer, L.
Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible.
Woyna, M.A.; Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.
The Hyperspectral Image Data Exploration Workbench (HIDEW) software system has been developed by Argonne National Laboratory to enable analysts at Unix workstations to conveniently access and manipulate high-resolution imagery data for analysis, mapping purposes, and input to environmental modeling applications. HIDEW is fully object-oriented, including the underlying database. This system was developed as an aid to site characterization work and atmospheric research projects
Woyna, M.A.; Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.
The Hyperspectral Image Data Exploration Workbench (HIDEW) software system has been developed by Argonne National Laboratory to enable analysts at Unix workstations to conveniently access and manipulate high-resolution imagery data for analysis, mapping purposes, and input to environmental modeling applications. HIDEW is fully object-oriented, including the underlying database. This system was developed as an aid to site characterization work and atmospheric research projects.
Habitat mapping and classification provides essential information for land use planning and ecosystem research, monitoring and management. At the Grand Bay National Estuarine Research Reserve (GRDNERR), Mississippi, habitat characterization of the Grand Bay watershed will also be used to develop a decision-support tool for the NERR's managers and state and local partners. Grand Bay NERR habitat units were identified using a combination of remotely sensed imagery, aerial photography and elevation data. Airborne Imaging Spectrometer for Applications (AISA) hyperspectral data, acquired 5 and 6 May 2010, was analyzed and classified using ENVI v4.8 and v5.0 software. The AISA system was configured to return 63 bands of digital imagery data with a spectral range of 400 to 970 nm (VNIR), spectral resolution (bandwidth) at 8.76 nm, and 1 m spatial resolution. Minimum Noise Fraction (MNF) and Inverse Minimum Noise Fraction were applied to the data prior to using Spectral Angle Mapper ([SAM] supervised) and ISODATA (unsupervised) classification techniques. The resulting class image was exported to ArcGIS 10.0 and visually inspected and compared with the original imagery as well as auxiliary datasets to assist in the attribution of habitat characteristics to the spectral classes, including: National Agricultural Imagery Program (NAIP) aerial photography, Jackson County, MS, 2010; USFWS National Wetlands Inventory, 2007; an existing GRDNERR habitat map (2004), SAV (2009) and salt panne (2002-2003) GIS produced by GRDNERR; and USACE lidar topo-bathymetry, 2005. A field survey to validate the map's accuracy will take place during the 2012 summer season. ENVI's Random Sample generator was used to generate GIS points for a ground-truth survey. The broad range of coastal estuarine habitats and geomorphological features- many of which are transitional and vulnerable to environmental stressors- that have been identified within the GRDNERR point to the value of the Reserve for
Liu, Yao; Chen, Yuehua; Tan, Kezhu; Xie, Hong; Wang, Liguo; Xie, Wu; Yan, Xiaozhen; Xu, Zhen
Band selection is considered to be an important processing step in handling hyperspectral data. In this work, we selected informative bands according to the maximal relevance minimal redundancy (MRMR) criterion based on neighborhood mutual information. Two measures MRMR difference and MRMR quotient were defined and a forward greedy search for band selection was constructed. The performance of the proposed algorithm, along with a comparison with other methods (neighborhood dependency measure based algorithm, genetic algorithm and uninformative variable elimination algorithm), was studied using the classification accuracy of extreme learning machine (ELM) and random forests (RF) classifiers on soybeans’ hyperspectral datasets. The results show that the proposed MRMR algorithm leads to promising improvement in band selection and classification accuracy. (paper)
Racek, František; Jobánek, Adam; Baláž, Teodor; Krejčí, Jaroslav
Pixelated camouflage patterns fulfill the role of both principles the matching and the disrupting that are exploited for blending the target into the background. It means that pixelated pattern should respect natural background in spectral and spatial characteristics embodied in micro and macro patterns. The HS imaging plays the similar, however the reverse role in the field of reconnaissance systems. The HS camera fundamentally records and extracts both the spectral and spatial information belonging to the recorded scenery. Therefore, the article deals with problems of hyperspectral (HS) imaging and subsequent processing of HS images of pixelated camouflage patterns which are among others characterized by their specific spatial frequency heterogeneity.
Wang, Yujie; Hu, Xin; Hou, Zhiwei; Ning, Jingming; Zhang, Zhengzhu
Nitrogen (N) fertilizer plays an important role in tea plantation management, with significant impacts on the photosynthetic capacity, productivity and nutrition status of tea plants. The present study aimed to establish a method for the discrimination of N fertilizer levels using hyperspectral imaging technique. Spectral data were extracted from the region of interest, followed by the first derivative to reduce background noise. Five optimal wavelengths were selected by principal component analysis. Texture features were extracted from the images at optimal wavelengths by gray-level gradient co-occurrence matrix. Support vector machine (SVM) and extreme learning machine were used to build classification models based on spectral data, optimal wavelengths, texture features and data fusion, respectively. The SVM model using fused data gave the best performance with highest correct classification rate of 100% for prediction set. The overall results indicated that visible and near-infrared hyperspectral imaging combined with SVM were effective in discriminating N fertilizer levels of tea plants. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.
Nansen, Christian; Zhao, Genpin; Dakin, Nicole; Zhao, Chunhui; Turner, Shane R
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
Carpena, Emmanuel; Jiménez, Luis O.; Arzuaga, Emmanuel; Fonseca, Sujeily; Reyes, Ernesto; Figueroa, Juan
Improved benthic habitat mapping is needed to monitor coral reefs around the world and to assist coastal zones management programs. A fundamental challenge to remotely sensed mapping of coastal shallow waters is due to the significant disparity in the optical properties of the water column caused by the interaction between the coast and the sea. The objects to be classified have weak signals that interact with turbid waters that include sediments. In real scenarios, the absorption and backscattering coefficients are unknown with different sources of variability (river discharges and coastal interactions). Under normal circumstances, another unknown variable is the depth of shallow waters. This paper presents the development of algorithms for retrieving information and its application to the classification and mapping of objects under coastal shallow waters with different unknown concentrations of sediments. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and classification of hyperspectral data. The algorithms developed were applied to one set of real hyperspectral imagery taken in a tank filled with water and TiO2 that emulates turbid coastal shallow waters. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the water tank using a priori information in the form of stored spectral signatures, previously measured, of objects of interest.
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)
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.
Ahn, Chi Kook; Cho, Byoung Kwan; Mo, Chang Yeon; Kim, Moon S.
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.
Full Text Available In this study, a 1-D Convolutional Neural Network (CNN architecture was developed, trained and utilized to classify single (summer and three seasons (spring, summer, fall of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF and Support Vector Machine (SVM classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM, 1.9% (CNN to 3.5% (RF over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.
Binol, Hamidullah; Bal, Abdullah
A novel hyperspectral target detection technique based on Fukunaga-Koontz transform (FKT) is presented. FKT offers significant properties for feature selection and ordering. However, it can only be used to solve multi-pattern classification problems. Target detection may be considered as a two-class classification problem, i.e., target versus background clutter. Nevertheless, background clutter typically contains different types of materials. That's why; target detection techniques are different than classification methods by way of modeling clutter. To avoid the modeling of the background clutter, we have improved one-class FKT (OC-FKT) for target detection. The statistical properties of target training samples are used to define tunnel-like boundary of the target class. Non-target samples are then created synthetically as to be outside of the boundary. Thus, only limited target samples become adequate for training of FKT. The hyperspectral image experiments confirm that the proposed OC-FKT technique provides an effective means for target detection.
Mo, Changyeun; Kim, Giyoung; Kim, Moon S; Lim, Jongguk; Cho, Hyunjeong; Barnaby, Jinyoung Yang; Cho, Byoung-Kwan
Non-destructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed to detect worms on fresh-cut lettuce. The optimal wavebands for detecting the worms were investigated using the one-way ANOVA and correlation analyses. The worm detection imaging algorithms, RSI-I (492-626)/492 , provided a prediction accuracy of 99.0%. The fluorescence HSI techniques indicated that the spectral images with a pixel size of 1 × 1 mm had the best classification accuracy for worms. The overall results demonstrate that fluorescence HSI techniques have the potential to detect worms on fresh-cut lettuce. In the future, we will focus on developing a multi-spectral imaging system to detect foreign substances such as worms, slugs and earthworms on fresh-cut lettuce. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.
Bae, Hyun Jin; Seo, Young Wook; Lohumi, Santosh; Park, Eun Soo; Cho, Byoung Kwan [Biosystems Machinery Engineering, Chungnam National University, Daejeon (Korea, Republic of); Kim, Dae Yong [Logistics institude, CJ Korea Express, Seoul (Korea, Republic of)
Seed viability is one of the most important parameters that is directly related with seed germination performance and seedling emergence. In this study, a hyperspectral imaging (HSI) system having a range of 1000 –2500 nm was used to classify viable watermelon seeds from nonviable seeds. In order to obtain nonviable watermelon seeds, a total of 96 seeds were artificially aged by immersing the seeds in hot water (25°C) for 15 days. Further, hyperspectral images for 192 seeds (96 normal and 96 aged) were acquired using the developed HSI system. A germination test was performed for all the 192 seeds in order to confirm their viability. Spectral data from the hyperspectral images of the seeds were extracted by selecting pixels from the region of interest. Each seed spectrum was averaged and preprocessed to develop a classification model of partial least square discriminant analysis (PLS-DA). The developed PLS-DA model showed a classification accuracy of 94.7% for the calibration set, and 84.2% for the validation set. The results demonstrate that the proposed technique can classify viable and nonviable watermelon seeds with a reasonable accuracy, and can be further converted into an online sorting system for rapid and nondestructive classification of watermelon seeds with regard to viability.
Bae, Hyun Jin; Seo, Young Wook; Lohumi, Santosh; Park, Eun Soo; Cho, Byoung Kwan; Kim, Dae Yong
Seed viability is one of the most important parameters that is directly related with seed germination performance and seedling emergence. In this study, a hyperspectral imaging (HSI) system having a range of 1000 –2500 nm was used to classify viable watermelon seeds from nonviable seeds. In order to obtain nonviable watermelon seeds, a total of 96 seeds were artificially aged by immersing the seeds in hot water (25°C) for 15 days. Further, hyperspectral images for 192 seeds (96 normal and 96 aged) were acquired using the developed HSI system. A germination test was performed for all the 192 seeds in order to confirm their viability. Spectral data from the hyperspectral images of the seeds were extracted by selecting pixels from the region of interest. Each seed spectrum was averaged and preprocessed to develop a classification model of partial least square discriminant analysis (PLS-DA). The developed PLS-DA model showed a classification accuracy of 94.7% for the calibration set, and 84.2% for the validation set. The results demonstrate that the proposed technique can classify viable and nonviable watermelon seeds with a reasonable accuracy, and can be further converted into an online sorting system for rapid and nondestructive classification of watermelon seeds with regard to viability
Full Text Available Ophiolitic regions are one of the most complex geology settings. Mapping in these parts need broad and precise studies and tools because of the mixture rocks and confusion units. Hyperion hyperspectral sensor data are one of the advanced tools for earth surface mapping that containing rich information of shallow electromagnetic reflection in 242 continuous bands. Because of some contaminated noise in tens of these bands we removed 87 most noisy bands and focused our study on 155 low noisy bands. In present study, tow spectral based classification algorithms of support vector machine and neutral network are compared on hyperion image for classification of cluttered units in an ophiolite set. Study area is Mesina region in collision ophiolitic belt of south east of Iran. In this region for design processing results validation rate, lots of random locations and control points were studied in field scale and were sampled for laboratory surveys. Samples were investigated in microscopic section and by electron microprobe system. Based on laboratory-field studies, the lithology of this area can divided into five general groups: (Melange series, metamorphic units, Oligocene – Miocene to Quaternary volcanic units, lime and flysch units. Based on field-laboratory works, some standard points defined for validate processing results accuracy rate. Therefore, the Support Vector Machine and neutral network method as advanced hyperspectral image processing methods respectively have overall accuracies of 52% and 65%. Consequently the method based neutral network theory for hyperspectral classification have acceptable ratio in separation of blended complicated units.
Ochoa, Daniel; Cevallos, Juan; Vargas, German; Criollo, Ronald; Romero, Dennis; Castro, Rodrigo; Bayona, Oswaldo
Black Sigatoka (BS) is a banana plant disease caused by the fungus Mycosphaerella fijiensis. BS symptoms can be observed at late infection stages. By that time, BS has probably spread to other plants. In this paper, we present our current work on building an hyper-spectral (HS) imaging system aimed at in-vivo detection of BS pre-symptomatic responses in banana leaves. The proposed imaging system comprises a motorized stage, a high-sensitivity VIS-NIR camera and an optical spectrograph. To capture images of the banana leaf, the stage's speed and camera's frame rate must be computed to reduce motion blur and to obtain the same resolution along both spatial dimensions of the resulting HS cube. Our continuous leaf scanning approach allows imaging leaves of arbitrary length with minimum frame loss. Once the images are captured, a denoising step is performed to improve HS image quality and spectral profile extraction.
An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...
Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.
It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neur...
Greer, John B.; Flake, J. C.
The emerging field of Compressive Sensing (CS) provides a new way to capture data by shifting the heaviest burden of data collection from the sensor to the computer on the user-end. This new means of sensing requires fewer measurements for a given amount of information than traditional sensors. We investigate the efficacy of CS for capturing HyperSpectral Imagery (HSI) remotely. We also introduce a new family of algorithms for constructing HSI from CS measurements with Split Bregman Iteration [Goldstein and Osher,2009]. These algorithms combine spatial Total Variation (TV) with smoothing in the spectral dimension. We examine models for three different CS sensors: the Coded Aperture Snapshot Spectral Imager-Single Disperser (CASSI-SD) [Wagadarikar et al.,2008] and Dual Disperser (CASSI-DD) [Gehm et al.,2007] cameras, and a hypothetical random sensing model closer to CS theory, but not necessarily implementable with existing technology. We simulate the capture of remotely sensed images by applying the sensor forward models to well-known HSI scenes - an AVIRIS image of Cuprite, Nevada and the HYMAP Urban image. To measure accuracy of the CS models, we compare the scenes constructed with our new algorithm to the original AVIRIS and HYMAP cubes. The results demonstrate the possibility of accurately sensing HSI remotely with significantly fewer measurements than standard hyperspectral cameras.
Annala, L.; Eskelinen, M. A.; Hämäläinen, J.; Riihinen, A.; Pölönen, I.
Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor ...
Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin
Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.
Love, Steven P.; Graff, David L.
A programmable, many-band spectral imager based on addressable spatial light modulators (ASLMs), such as micro-mirror-, micro-shutter- or liquid-crystal arrays, is described. Capable of collecting at once, without scanning, a complete two-dimensional spatial image with ASLM spectral processing applied simultaneously to the entire image, the invention employs optical assemblies wherein light from all image points is forced to impinge at the same angle onto the dispersing element, eliminating interplay between spatial position and wavelength. This is achieved, as examples, using telecentric optics to image light at the required constant angle, or with micro-optical array structures, such as micro-lens- or capillary arrays, that aim the light on a pixel-by-pixel basis. Light of a given wavelength then emerges from the disperser at the same angle for all image points, is collected at a unique location for simultaneous manipulation by the ASLM, then recombined with other wavelengths to form a final spectrally-processed image.
Full Text Available For this research, the researchers examine various existing image classification algorithms with the aim of demonstrating how these algorithms can be applied to remote sensing images. These algorithms are broadly divided into supervised...
Hinnrichs, Michele; Hinnrichs, Bradford; McCutchen, Earl
Pacific Advanced Technology (PAT) has developed an infrared hyperspectral camera, both MWIR and LWIR, small enough to serve as a payload on a miniature unmanned aerial vehicles. The optical system has been integrated into the cold-shield of the sensor enabling the small size and weight of the sensor. This new and innovative approach to infrared hyperspectral imaging spectrometer uses micro-optics and will be explained in this paper. The micro-optics are made up of an area array of diffractive optical elements where each element is tuned to image a different spectral region on a common focal plane array. The lenslet array is embedded in the cold-shield of the sensor and actuated with a miniature piezo-electric motor. This approach enables rapid infrared spectral imaging with multiple spectral images collected and processed simultaneously each frame of the camera. This paper will present our optical mechanical design approach which results in an infrared hyper-spectral imaging system that is small enough for a payload on a mini-UAV or commercial quadcopter. The diffractive optical elements used in the lenslet array are blazed gratings where each lenslet is tuned for a different spectral bandpass. The lenslets are configured in an area array placed a few millimeters above the focal plane and embedded in the cold-shield to reduce the background signal normally associated with the optics. We have developed various systems using a different number of lenslets in the area array. Depending on the size of the focal plane and the diameter of the lenslet array will determine the spatial resolution. A 2 x 2 lenslet array will image four different spectral images of the scene each frame and when coupled with a 512 x 512 focal plane array will give spatial resolution of 256 x 256 pixel each spectral image. Another system that we developed uses a 4 x 4 lenslet array on a 1024 x 1024 pixel element focal plane array which gives 16 spectral images of 256 x 256 pixel resolution each
Livens, S.; Pauly, K.; Baeck, P.; Blommaert, J.; Nuyts, D.; Zender, J.; Delauré, B.
Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS) is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600-900 nm) in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots), horticulture (crop status monitoring to evaluate irrigation management in strawberry fields) and geology (meteorite detection on a grassland field). Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475-925 nm), and we discuss future work.
Full Text Available Imaging with a conventional frame camera from a moving remotely piloted aircraft system (RPAS is by design very inefficient. Less than 1 % of the flying time is used for collecting light. This unused potential can be utilized by an innovative imaging concept, the spatio-spectral camera. The core of the camera is a frame sensor with a large number of hyperspectral filters arranged on the sensor in stepwise lines. It combines the advantages of frame cameras with those of pushbroom cameras. By acquiring images in rapid succession, such a camera can collect detailed hyperspectral information, while retaining the high spatial resolution offered by the sensor. We have developed two versions of a spatio-spectral camera and used them in a variety of conditions. In this paper, we present a summary of three missions with the in-house developed COSI prototype camera (600–900 nm in the domains of precision agriculture (fungus infection monitoring in experimental wheat plots, horticulture (crop status monitoring to evaluate irrigation management in strawberry fields and geology (meteorite detection on a grassland field. Additionally, we describe the characteristics of the 2nd generation, commercially available ButterflEYE camera offering extended spectral range (475–925 nm, and we discuss future work.
Guerra, Raúl; López, Sebastián.; Callico, Gustavo M.; López, Jose F.; Sarmiento, Roberto
Endmember extraction and abundances calculation represent critical steps within the process of linearly unmixing a given hyperspectral image because of two main reasons. The first one is due to the need of computing a set of accurate endmembers in order to further obtain confident abundance maps. The second one refers to the huge amount of operations involved in these time-consuming processes. This work proposes an algorithm to estimate the endmembers of a hyperspectral image under analysis and its abundances at the same time. The main advantage of this algorithm is its high parallelization degree and the mathematical simplicity of the operations implemented. This algorithm estimates the endmembers as virtual pixels. In particular, the proposed algorithm performs the descent gradient method to iteratively refine the endmembers and the abundances, reducing the mean square error, according with the linear unmixing model. Some mathematical restrictions must be added so the method converges in a unique and realistic solution. According with the algorithm nature, these restrictions can be easily implemented. The results obtained with synthetic images demonstrate the well behavior of the algorithm proposed. Moreover, the results obtained with the well-known Cuprite dataset also corroborate the benefits of our proposal.
Qin, Jianwei; Lu, Renfu
The presence of pits in processed cherry products causes safety concerns for consumers and imposes potential liability for the food industry. The objective of this research was to investigate a hyperspectral transmission imaging technique for detecting the pit in tart cherries. A hyperspectral imaging system was used to acquire transmission images from individual cherry fruit for four orientations before and after pits were removed over the spectral region between 450 nm and 1,000 nm. Cherries of three size groups (small, intermediate, and large), each with two color classes (light red and dark red) were used for determining the effect of fruit orientation, size, and color on the pit detection accuracy. Additional cherries were studied for the effect of defect (i.e., bruises) on the pit detection. Computer algorithms were developed using the neural network (NN) method to classify the cherries with and without the pit. Two types of data inputs, i.e., single spectra and selected regions of interest (ROIs), were compared. The spectral region between 690 nm and 850 nm was most appropriate for cherry pit detection. The NN with inputs of ROIs achieved higher pit detection rates ranging from 90.6% to 100%, with the average correct rate of 98.4%. Fruit orientation and color had a small effect (less than 1%) on pit detection. Fruit size and defect affected pit detection and their effect could be minimized by training the NN with properly selected cherry samples.
Uygur, Merve; Karaman, Muhittin; Kumral, Mustafa
Çürüksu (Denizli) Graben hosts various geothermal fields such as Kızıldere, Yenice, Gerali, Karahayıt, and Tekkehamam. Neotectonic activities, which are caused by extensional tectonism, and deep circulation in sub-volcanic intrusions are heat sources of hydrothermal solutions. The temperature of hydrothermal solutions is between 53 and 260 degree Celsius. Phyllic, argillic, silicic, and carbonatization alterations and various hydrothermal minerals have been identified in various research studies of these areas. Surfaced hydrothermal alteration minerals are one set of potential indicators of geothermal resources. Developing the exploration tools to define the surface indicators of geothermal fields can assist in the recognition of geothermal resources. Thermal and hyperspectral imaging and analysis can be used for defining the surface indicators of geothermal fields. This study tests the hypothesis that hyperspectral image analysis based on EO-1 Hyperion images can be used for the delineation and definition of surfaced hydrothermal alteration in geothermal fields. Hyperspectral image analyses were applied to images covering the geothermal fields whose alteration characteristic are known. To reduce data dimensionality and identify spectral endmembers, Kruse's multi-step process was applied to atmospherically and geometrically-corrected hyperspectral images. Minimum Noise Fraction was used to reduce the spectral dimensions and isolate noise in the images. Extreme pixels were identified from high order MNF bands using the Pixel Purity Index. n-Dimensional Visualization was utilized for unique pixel identification. Spectral similarities between pixel spectral signatures and known endmember spectrum (USGS Spectral Library) were compared with Spectral Angle Mapper Classification. EO-1 Hyperion hyperspectral images and hyperspectral analysis are sensitive to hydrothermal alteration minerals, as their diagnostic spectral signatures span the visible and shortwave
Bostater, Charles R.; Oney, Taylor
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.
Junaid, Saher; Tomko, Jan; Semtsiv, Mykhaylo P.
quantum cascade laser illumination. AgGaS2 is used as the nonlinear medium for sum frequency generation using a 1064 nm mixing laser. Angular scanning of the nonlinear crystal provides broad spectral coverage at every spatial position in the image. This study demonstrates the retrieval of series...
Full Text Available Striped stem-borer (SSB infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI. Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA. Principal component analysis (PCA was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM were extracted from the first two principal component (PC images. A back-propagation neural network (BPNN was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation.
Samarov, Daniel; Wehner, Eleanor; Schwarz, Roderich; Zuzak, Karel; Livingston, Edward
Over the course of the last several years hyperspectral imaging (HSI) has seen increased usage in biomedicine. Within the medical field in particular HSI has been recognized as having the potential to make an immediate impact by reducing the risks and complications associated with laparotomies (surgical procedures involving large incisions into the abdominal wall) and related procedures. There are several ongoing studies focused on such applications. Hyperspectral images were acquired during pancreatoduodenectomies (commonly referred to as Whipple procedures), a surgical procedure done to remove cancerous tumors involving the pancreas and gallbladder. As a result of the complexity of the local anatomy, identifying where the common bile duct (CBD) is can be difficult, resulting in comparatively high incidents of injury to the CBD and associated complications. It is here that HSI has the potential to help reduce the risk of such events from happening. Because the bile contained within the CBD exhibits a unique spectral signature, we are able to utilize HSI segmentation algorithms to help in identifying where the CBD is. In the work presented here we discuss approaches to this segmentation problem and present the results.
Gong, Aiping; Zhu, Susu; He, Yong; Zhang, Chu
Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874–1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages. PMID:28757578
Huang, Shih-Wei; Chen, Shih-Hua; Chen, Weichung; Wu, I.-Chen; Wu, Ming Tsang; Kuo, Chie-Tong; Wang, Hsiang-Chen
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.
Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.
Browning, Craig M.; Mayes, Samuel; Rich, Thomas C.; Leavesley, Silas J.
Hyperspectral imaging (HSI) is a technology used in remote sensing, food processing and documentation recovery. Recently, this approach has been applied in the medical field to spectrally interrogate regions of interest within respective substrates. In spectral imaging, a two (spatial) dimensional image is collected, at many different (spectral) wavelengths, to sample spectral signatures from different regions and/or components within a sample. Here, we report on the use of hyperspectral imaging for endoscopic applications. Colorectal cancer is the 3rd leading cancer for incidences and deaths in the US. One factor of severity is the miss rate of precancerous/flat lesions ( 65% accuracy). Integrating HSI into colonoscopy procedures could minimize misdiagnosis and unnecessary resections. We have previously reported a working prototype light source with 16 high-powered light emitting diodes (LEDs) capable of high speed cycling and imaging. In recent testing, we have found our current prototype is limited by transmission loss ( 99%) through the multi-furcated solid light guide (lightpipe) and the desired framerate (20-30 fps) could not be achieved. Here, we report on a series of experimental and modeling studies to better optimize the lightpipe and the spectral endoscopy system as a whole. The lightpipe was experimentally evaluated using an integrating sphere and spectrometer (Ocean Optics). Modeling the lightpipe was performed using Monte Carlo optical ray tracing in TracePro (Lambda Research Corp.). Results of these optimization studies will aid in manufacturing a revised prototype with the newly designed light guide and increased sensitivity. Once the desired optical output (5-10 mW) is achieved then the HIS endoscope system will be able to be implemented without adding onto the procedure time.
Chaudhari, Abhijit J; Darvas, Felix; Bading, James R; Moats, Rex A; Conti, Peter S; Smith, Desmond J; Cherry, Simon R; Leahy, Richard M
For bioluminescence imaging studies in small animals, it is important to be able to accurately localize the three-dimensional (3D) distribution of the underlying bioluminescent source. The spectrum of light produced by the source that escapes the subject varies with the depth of the emission source because of the wavelength-dependence of the optical properties of tissue. Consequently, multispectral or hyperspectral data acquisition should help in the 3D localization of deep sources. In this paper, we describe a framework for fully 3D bioluminescence tomographic image acquisition and reconstruction that exploits spectral information. We describe regularized tomographic reconstruction techniques that use semi-infinite slab or FEM-based diffusion approximations of photon transport through turbid media. Singular value decomposition analysis was used for data dimensionality reduction and to illustrate the advantage of using hyperspectral rather than achromatic data. Simulation studies in an atlas-mouse geometry indicated that sub-millimeter resolution may be attainable given accurate knowledge of the optical properties of the animal. A fixed arrangement of mirrors and a single CCD camera were used for simultaneous acquisition of multispectral imaging data over most of the surface of the animal. Phantom studies conducted using this system demonstrated our ability to accurately localize deep point-like sources and show that a resolution of 1.5 to 2.2 mm for depths up to 6 mm can be achieved. We also include an in vivo study of a mouse with a brain tumour expressing firefly luciferase. Co-registration of the reconstructed 3D bioluminescent image with magnetic resonance images indicated good anatomical localization of the tumour
Ceamanos, X.; Doute, S.
The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by a common artifact in "push-broom" sensors, the so-called "spectral smile". As a consequence, both central wavelength and spectral width of the spectral response vary along the across-track dimension, thus giving rise to a shifting and smoothing of spectra (see Fig. 1 (left)). In fact, both effects are greater for spectra on the edges, while they are minimum for data acquired by central detectors, the so-called "sweet spot". The prior artifacts become particularly critical for Martian observations which contain steep spectra such as CO2 ice-rich polar images. Fig. 1 (right) shows the horizontal brightness gradient which appears in every band corresponding to a steep portion of spectra. The correction of CRISM spectral smile is addressed using a two-step method which aims at modifying data sensibly in order to mimic the optimal CRISM response. First, all spectra, which are previously interpolated by cubic splines, are resampled to the "sweet spot" wavelengths in order to overcome the spectra shift. Secondly, the non-uniform spectral width is overcome by mimicking an increase of spectral resolution thanks to a spectral sharpening. In order to minimize noise, only bands particularly suffering from smile are selected. First, bands corresponding to the outliers of the Minimum Noise Transformation (MNF) eigenvector, which corresponds to the MNF band related to smile (MNF-smile), are selected. Then, a spectral neighborhood Θi, which takes into account the local spectral convexity or concavity, is defined for every selected band in order to maximize spectral shape preservation. The proposed sharpening technique takes into account both the instrument parameters and the observed spectra. First, every reflectance value belonging to a Θi is reevaluated by a sharpening which depends on a ratio of the spectral width of the current detector and the "sweet spot" one. Then, the optimal degree of
Mehta, Nishir; Mahigir, Amirreza; Veronis, Georgios; Gartia, Manas Ranjan
Orientation of plasmonic nanostructures is an important feature in many nanoscale applications such as catalyst, biosensors DNA interactions, protein detections, hotspot of surface enhanced Raman spectroscopy (SERS), and fluorescence resonant energy transfer (FRET) experiments. However, due to diffraction limit, it is challenging to obtain the exact orientation of the nanostructure using standard optical microscope. Hyperspectral Imaging Microscopy is a state-of-the-art visualization technology that combines modern optics with hyperspectral imaging and computer system to provide the identification and quantitative spectral analysis of nano- and microscale structures. In this work, initially we use transmitted dark field imaging technique to locate single nanoparticle on a glass substrate. Then we employ hyperspectral imaging technique at the same spot to investigate orientation of single nanoparticle. No special tagging or staining of nanoparticle has been done, as more likely required in traditional microscopy techniques. Different orientations have been identified by carefully understanding and calibrating shift in spectral response from each different orientations of similar sized nanoparticles. Wavelengths recorded are between 300 nm to 900 nm. The orientations measured by hyperspectral microscopy was validated using finite difference time domain (FDTD) electrodynamics calculations and scanning electron microscopy (SEM) analysis. The combination of high resolution nanometer-scale imaging techniques and the modern numerical modeling capacities thus enables a meaningful advance in our knowledge of manipulating and fabricating shaped nanostructures. This work will advance our understanding of the behavior of small nanoparticle clusters useful for sensing, nanomedicine, and surface sciences.
Wang, Biao; Lin, Jia-xuan; Gao, Wei; Yue, Hui
Remote sensing image simulation plays an important role in spaceborne/airborne load demonstration and algorithm development. Hyperspectral imaging is valuable in marine monitoring, search and rescue. On the demand of spectral imaging of objects under the complex sea scene, physics based simulation method of spectral image of object under sea scene is proposed. On the development of an imaging simulation model considering object, background, atmosphere conditions, sensor, it is able to examine the influence of wind speed, atmosphere conditions and other environment factors change on spectral image quality under complex sea scene. Firstly, the sea scattering model is established based on the Philips sea spectral model, the rough surface scattering theory and the water volume scattering characteristics. The measured bi directional reflectance distribution function (BRDF) data of objects is fit to the statistical model. MODTRAN software is used to obtain solar illumination on the sea, sky brightness, the atmosphere transmittance from sea to sensor and atmosphere backscattered radiance, and Monte Carlo ray tracing method is used to calculate the sea surface object composite scattering and spectral image. Finally, the object spectrum is acquired by the space transformation, radiation degradation and adding the noise. The model connects the spectrum image with the environmental parameters, the object parameters, and the sensor parameters, which provide a tool for the load demonstration and algorithm development.
Khan, Zohaib; Shafait, Faisal; Mian, Ajmal
A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.
Mayes, Sam A.; Leavesley, Silas J.; Rich, Thomas C.
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.
Milanic, Matija; Bjorgan, Asgeir; Larsson, Marcus; Strömberg, Tomas; Randeberg, Lise L.
Hypercholesterolemia is characterized by high blood levels of cholesterol and is associated with increased risk of atherosclerosis and cardiovascular disease. Xanthelasma is a subcutaneous lesion appearing in the skin around the eyes. Xanthelasma is related to hypercholesterolemia. Identifying micro-xanthelasma can thereforeprovide a mean for early detection of hypercholesterolemia and prevent onset and progress of disease. The goal of this study was to investigate spectral and spatial characteristics of hypercholesterolemia in facial skin. Optical techniques like hyperspectral imaging (HSI) might be a suitable tool for such characterization as it simultaneously provides high resolution spatial and spectral information. In this study a 3D Monte Carlo model of lipid inclusions in human skin was developed to create hyperspectral images in the spectral range 400-1090 nm. Four lesions with diameters 0.12-1.0 mm were simulated for three different skin types. The simulations were analyzed using three algorithms: the Tissue Indices (TI), the two layer Diffusion Approximation (DA), and the Minimum Noise Fraction transform (MNF). The simulated lesions were detected by all methods, but the best performance was obtained by the MNF algorithm. The results were verified using data from 11 volunteers with known cholesterol levels. The face of the volunteers was imaged by a LCTF system (400- 720 nm), and the images were analyzed using the previously mentioned algorithms. The identified features were then compared to the known cholesterol levels of the subjects. Significant correlation was obtained for the MNF algorithm only. This study demonstrates that HSI can be a promising, rapid modality for detection of hypercholesterolemia.
Fabelo, Himar; Ortega, Samuel; Kabwama, Silvester; Callico, Gustavo M.; Bulters, Diederik; Szolna, Adam; Pineiro, Juan F.; Sarmiento, Roberto
Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.
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.
Full Text Available Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN, evolutionary neural network (ENN, extreme learning machine (ELM, general regression neural network (GRNN and radial basis neural network (RBNN were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.
Wang, Yueming; Hu, Weida; Shu, Rong; Li, Chunlai; Yuan, Liyin; Wang, Jianyu
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.
Franke, Jonas; Menz, Gunter; Oerke, Erich-Christian; Rascher, Uwe
In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.
Candiani, Gabriele; Picone, Nicoletta; Pompilio, Loredana; Pepe, Monica; Colledani, Marcello
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
In this study, hyperspectral fluorescence imaging techniques were investigated for detection of microbial biofilm on stainless steel plates typically used to manufacture food processing equipment. Stainless steel coupons were immersed in bacterium cultures consisting of nonpathogenic E. coli, Pseudo...
Choi, Young-Wan; Yang, Seung-Uk; Kang, Myung-Seok; Kim, Ee-Eul
Funded by the Ministry of Commerce, Industry, and Energy of Korea, SI initiated the development of the prototype model of TMA-based electro-optical system as part of the national space research and development program. Its optical aperture diameter is 120 mm, the effective focal length is 462 mm, and its full field-of-view is 5.08 degrees. The dimension is of about 600 mm × 400 mm × 400 mm and the weight is less than 15 kg. To demonstrate its performance, hyper-spectral imaging based on linear spectral filter is selected for the application of the prototype. The spectral resolution will be less than 10 nm and the number of channels will be more than 40 in visible and nearinfrared region. In this paper, the progress made so far on the prototype development will be presented
Pisani, Marco; Zucco, Massimo
In the last decades Hyperspectral Imager (HI) have become irreplaceable space-borne instruments for an increasing number of applications. A number of HIs are now operative onboard (e.g. CHRIS on PROBA), others are going to be launched (e.g. PRISMA, EnMAP, HyspIRI), many others are at the breadboard level. The researchers goal is to realize HI with high spatial and spectral resolution, having low weight and contained dimensions. The most common HI technique is based on the use of a dispersive mean (a grating or a prism) or on the use of band pass filters (tunable or linear variable). These approaches have the advantages of allowing compact devices. Another approach is based on the use of interferometer based spectrometers (Michelson or Sagnac type). The advantage of the latter is a very high efficiency in light collection because of the well-known Felgett and Jaquinot principles.
Luan, Kuifeng; Tong, Xiaohua; Liu, Xiangfeng; Ma, Yanhua; Shu, Rong; Xu, Weiming
Geometric correction without ground control points (GCPs) is a very important topic. Conventional airborne photogrammetry is difficult to implement in areas where the installation of GCPs is not available. The technical of integrated GPS/INS systems providing the positioning and attitude of airborne systems is a potential solution in such areas. This paper first states the principle of geometric correction based on a combination of GPS and INS then the error of the geometric correction of Pushbroom Hyperspectral Imager (PHI) without GCP was analysed, then a flight test was carried out in an area of Damxung, Tibet. The experiment result showed that the error at straight track was small, generally less than 1 pixel, while the maximum error at cross track direction, was close to 2 pixels. The results show that geometric correction of PHI without GCP enables a variety of mapping products to be generated from airborne navigation and imagery data
Full Text Available Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.
Annala, L.; Eskelinen, M. A.; Hämäläinen, J.; Riihinen, A.; Pölönen, I.
Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.
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 o...
Full Text Available Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to “the curse of dimensionality”. In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA, guided image filtering and the random forest classifier (RF. In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.
Wilson, A.; Lindholm, D. M.; Coddington, O.; Pilewskie, P.
Hyperspectral image volumes are very large. A hyperspectral image analysis (HIA) may use 100TB of data, a huge barrier to their use. Hylatis is a new NASA project to create a toolset for HIA. Through web notebook and cloud technology, Hylatis will provide a more interactive experience for HIA by defining and implementing concepts and operations for HIA, identified and vetted by subject matter experts, and callable within a general purpose language, particularly Python. Hylatis leverages LaTiS, a data access framework developed at LASP. With an OPeNDAP compliant interface plus additional server side capabilities, the LaTiS API provides a uniform interface to virtually any data source, and has been applied to various storage systems, including: file systems, databases, remote servers, and in various domains including: space science, systems administration and stock quotes. In the LaTiS architecture, data `adapters' read data into a data model, where server-side computations occur. Data `writers' write data from the data model into the desired format. The Hylatis difference is the data model. In LaTiS, data are represented as mathematical functions of independent and dependent variables. Domain semantics are not present at this level, but are instead present in higher software layers. The benefit of a domain agnostic, mathematical representation is having the power of math, particularly functional algebra, unconstrained by domain semantics. This agnosticism supports reusable server side functionality applicable in any domain, such as statistical, filtering, or projection operations. Algorithms to aggregate or fuse data can be simpler because domain semantics are separated from the math. Hylatis will map the functional model onto the Spark relational interface, thereby adding a functional interface to that big data engine.This presentation will discuss Hylatis goals, strategies, and current state.
Bungert, Leon; Coomes, David A.; Ehrhardt, Matthias J.; Rasch, Jennifer; Reisenhofer, Rafael; Schönlieb, Carola-Bibiane
Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.
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
McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.
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.
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.
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.
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.
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
Lin, Ping; Chen, Yong-ming; Yao, Zhi-lei
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
Everard, Colm D.; Kim, Moon S.; Lee, Hoyoung
The production of contaminant free fresh fruit and vegetables is needed to reduce foodborne illnesses and related costs. Leafy greens grown in the field can be susceptible to fecal matter contamination from uncontrolled livestock and wild animals entering the field. Pathogenic bacteria can be transferred via fecal matter and several outbreaks of E.coli O157:H7 have been associated with the consumption of leafy greens. This study examines the use of hyperspectral fluorescence imaging coupled with multivariate image analysis to detect fecal contamination on Spinach leaves (Spinacia oleracea). Hyperspectral fluorescence images from 464 to 800 nm were captured; ultraviolet excitation was supplied by two LED-based line light sources at 370 nm. Key wavelengths and algorithms useful for a contaminant screening optical imaging device were identified and developed, respectively. A non-invasive screening device has the potential to reduce the harmful consequences of foodborne illnesses.
T. H. Kurz
Full Text Available Compact and lightweight hyperspectral imagers allow the application of close range hyperspectral imaging with a ground based scanning setup for geological fieldwork. Using such a scanning setup, steep cliff sections and quarry walls can be scanned with a more appropriate viewing direction and a higher image resolution than from airborne and spaceborne platforms. Integration of the hyperspectral imagery with terrestrial lidar scanning provides the hyperspectral information in a georeferenced framework and enables measurement at centimetre scale. In this paper, three geological case studies are used to demonstrate the potential of this method for rock characterisation. Two case studies are applied to carbonate quarries where mapping of different limestone and dolomite types was required, as well as measurements of faults and layer thicknesses from inaccessible parts of the quarries. The third case study demonstrates the method using artificial lighting, applied in a subsurface scanning scenario where solar radiation cannot be utilised.
Balooch, Ali; Nazeri, Majid; Abbasi, Hamed
In the present work, a hyperspectral imaging system (imaging spectrometer) using a commercial webcam has been designed and developed. This system was able to capture two-dimensional spectra (in emission, transmission and reflection modes) directly from the scene in the desired wavelengths. Imaging of the object is done directly by linear sweep (pushbroom method). To do so, the spectrometer is equipped with a suitable collecting lens and a linear travel stage. A 1920 x 1080 pixel CMOS webcam was used as a detector. The spectrometer has been calibrated by the reference spectral lines of standard lamps. The spectral resolution of this system was about 2nm and its spatial resolution was about 1 mm for a 10 cm long object. The hardware solution is based on data acquisition working on the USB platform and controlled by a LabVIEW program. In this system, the initial output was a three-dimensional matrix in which two dimensions of the matrix were related to the spatial information of the object and the third dimension was the spectrum of any point of the object. Finally, the images in different wavelengths were created by reforming the data of the matrix. The free spectral range (FSR) of the system was 400 to 1100 nm. The system was successfully tested for some applications, such as plasma diagnosis as well as applications in food and agriculture sciences.
Full Text Available Data fusion techniques require a good registration of all the used datasets. In remote sensing, images are usually geo-referenced using the GPS and IMU data. However, if more precise registration is required, image processing techniques can be employed. We propose a method for multi-modal image coregistration between hyperspectral images (HSI and digital surface models (DSM. The method is divided in three parts: object and line detection of the same object in HSI and DSM, line matching and determination of transformation parameters. Homogeneous coordinates are used to implement matching and adjustment of transformation parameters. The common object in HSI and DSM are building boundaries. They have apparent change in height and material, that can be detected in DSM and HSI, respectively. Thus, before the matching and transformation parameter computation, building outlines are detected and adjusted in HSI and DSM. We test the method on a HSI and two DSM, using extracted building outbounds and for comparison also extracted lines with a line detector. The results show that estimated building boundaries provide more line assignments, than using line detector.
Yang, Xue; Li, Gang; Lin, Ling
Multiple diseases such as breast tumor poses a great threat to women's health and life, while the traditional detection method is complex, costly and unsuitable for frequently self-examination, therefore, an inexpensive, convenient and efficient method for tumor self-inspection is needed urgently, and lesion localization is an important step. This paper proposes an self-examination method for positioning of a lesion. The method adopts transillumination to acquire the hyperspectral images and to assess the spatial information of lesion. Firstly, multi-wavelength sources are modulated with frequency division, which is advantageous to separate images of different wavelength, meanwhile, the source serves as fill light to each other to improve the sensitivity in the low-lightlevel imaging. Secondly, the signal-to-noise ratio of transmitted images after demodulation are improved by frame accumulation technology. Next, gray distributions of transmitted images are analyzed. The gray-level differences is constituted by the actual transmitted images and fitting transmitted images of tissue without lesion, which is to rule out individual differences. Due to scattering effect, there will be transition zones between tissue and lesion, and the zone changes with wavelength change, which will help to identify the structure details of lesion. Finally, image segmentation is adopted to extract the lesion and the transition zones, and the spatial features of lesion are confirmed according to the transition zones and the differences of transmitted light intensity distributions. Experiment using flat-shaped tissue as an example shows that the proposed method can extract the space information of lesion.
Paul, Subir; Nagesh Kumar, D.
Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.
Peter R. Robichaud; Sarah A. Lewis; Denise Y. M. Laes; Andrew T. Hudak; Raymond F. Kokaly; Joseph A. Zamudio
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...
Keymeulen, Didlier; Aranki, Nazeeh I.; Klimesh, Matthew A.; Bakhshi, Alireza
Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx
Full Text Available This paper deals with the application of distributed source coding (DSC theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.
A microscopic hyperspectral imager was developed based on the microscopic technology and the spectral imaging technology. Some microscopic hyperspectral images of retina sections of the normal, the diabetic, and the treated rats were collected by the new imager. Single-band images and pseudo-color images of each group were obtained and the typical transmittance spectrums were ex-tracted. The results showed that the transmittance of outer nuclear layer cells of the diabetic group was generally higher than that of the normal. A small absorption peak appeared near the 180th band in the spectrum of the diabetic group and this peak weakened or disappeared in the spectrum of the treated group. Our findings indicate that the microscopic hyperspectral images include wealthy information of retina sections which is helpful for the ophthalmologist to reveal the pathogenesis of diabetic reti-nopathy and explore the therapeutic effect of drugs.
Full Text Available The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1 opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m to EO-1 Hyperion (with a spatial resolution of 30 m. The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC, pixel-based support vector machine (PSVM, hybrid maximum likelihood classifier (HMLC, and hybrid support vector machine (HSVM were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the
Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing
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.
Yu, Chaoyin; Yuan, Zhengwu; Wu, Yuanfeng
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.
Davis, Curtiss O.; Kappus, Mary E.; Bowles, Jeffrey H.; Fisher, John; Antoniades, John A.; Carney, Megan
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.
Guerra, Raúl; López, Sebastián.; Sarmiento, Roberto
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.
Guillemot, Mathilde; Midahuen, Rony; Archeny, Delpine; Fulchiron, Corine; Montvernay, Regis; Perrin, Guillaume; Leroux, Denis F.
BioMérieux is automating the microbiology laboratory in order to reduce cost (less manpower and consumables), to improve performance (increased sensitivity, machine algorithms) and to gain traceability through optimization of the clinical laboratory workflow. In this study, we evaluate the potential of Hyperspectral imaging (HSI) as a substitute to human visual observation when performing the task of microbiological culture interpretation. Microbial colonies from 19 strains subcategorized in 6 chromogenic classes were analyzed after a 24h-growth on a chromogenic culture medium (chromID® CPS Elite, bioMérieux, France). The HSI analysis was performed in the VNIR region (400-900 nm) using a linescan configuration. Using algorithms relying on Linear Spectral Unmixing, and using exclusively Diffuse Reflectance Spectra (DRS) as input data, we report interclass classification accuracies of 100% using a fully automatable approach and no use of morphological information. In order to eventually simplify the instrument, the performance of degraded DRS was also evaluated using only the most discriminant 14 spectral channels (a model for a multispectral approach) or 3 channels (model of a RGB image). The overall classification performance remains unchanged for our multispectral model but is degraded for the predicted RGB model, hints that a multispectral solution might bring the answer for an improved colony recognition.
Barducci, Alessandro; Guzzi, Donatella; Lastri, Cinzia; Nardino, Vanni; Marcoionni, Paolo; Pippi, Ivan
Compressive sensing (CS) is a new technology that investigates the chance to sample signals at a lower rate than the traditional sampling theory. The main advantage of CS is that compression takes place during the sampling phase, making possible significant savings in terms of the ADC, data storage memory, down-link bandwidth, and electrical power absorption. The CS technology could have primary importance for spaceborne missions and technology, paving the way to noteworthy reductions of payload mass, volume, and cost. On the contrary, the main CS disadvantage is made by the intensive off-line data processing necessary to obtain the desired source estimation. In this paper we summarize the CS architecture and its possible implementations for Earth observation, giving evidence of possible bottlenecks hindering this technology. CS necessarily employs a multiplexing scheme, which should produce some SNR disadvantage. Moreover, this approach would necessitate optical light modulators and 2-dim detector arrays of high frame rate. This paper describes the development of a sensor prototype at laboratory level that will be utilized for the experimental assessment of CS performance and the related reconstruction errors. The experimental test-bed adopts a push-broom imaging spectrometer, a liquid crystal plate, a standard CCD camera and a Silicon PhotoMultiplier (SiPM) matrix. The prototype is being developed within the framework of the ESA ITI-B Project titled "Hyperspectral Passive Satellite Imaging via Compressive Sensing".
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.
Full Text Available Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified with some mathematical equations. The spatial constraint allows taking into account the inherent spatial relationships of any image and its color. This approach provides effective PSNR for the segmented image. These results have the better performance as the segmented images are compared with Watershed & Region Growing Algorithm and provide effective segmentation for the Spectral Images & Medical Images.
Mo, Changyeun; Kim, Giyoung; Kim, Moon S.; Lim, Jongguk; Lee, Seung Hyun; Lee, Hong-Seok; Cho, Byoung-Kwan
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.
Deal, Joshua; Favreau, Peter F.; Lopez, Carmen; Lall, Malvika; Weber, David S.; Rich, Thomas C.; Leavesley, Silas J.
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.
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.
Laurence, Audrey; Pichette, Julien; Angulo-Rodríguez, Leticia M.; Saint Pierre, Catherine; Lesage, Frédéric; Bouthillier, Alain; Nguyen, Dang Khoa; Leblond, Frédéric
Following normal neuronal activity, there is an increase in cerebral blood flow and cerebral blood volume to provide oxygenated hemoglobin to active neurons. For abnormal activity such as epileptiform discharges, this hemodynamic response may be inadequate to meet the high metabolic demands. To verify this hypothesis, we developed a novel hyperspectral imaging system able to monitor real-time cortical hemodynamic changes during brain surgery. The imaging system is directly integrated into a surgical microscope, using the white-light source for illumination. A snapshot hyperspectral camera is used for detection (4x4 mosaic filter array detecting 16 wavelengths simultaneously). We present calibration experiments where phantoms made of intralipid and food dyes were imaged. Relative concentrations of three dyes were recovered at a video rate of 30 frames per second. We also present hyperspectral recordings during brain surgery of epileptic patients with concurrent electrocorticography recordings. Relative concentration maps of oxygenated and deoxygenated hemoglobin were extracted from the data, allowing real-time studies of hemodynamic changes with a good spatial resolution. Finally, we present preliminary results on phantoms obtained with an integrated spatial frequency domain imaging system to recover tissue optical properties. This additional module, used together with the hyperspectral imaging system, will allow quantification of hemoglobin concentrations maps. Our hyperspectral imaging system offers a new tool to analyze hemodynamic changes, especially in the case of epileptiform discharges. It also offers an opportunity to study brain connectivity by analyzing correlations between hemodynamic responses of different tissue regions.
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.
Becker, J.K.; Marschall, P.; Brunner, P.; Cholet, C.; Renard, P.; Buckley, S.; Kurz, T.
, 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
Jafari-Saraf, Lida; Gordon, Ian L
To evaluate the correlation between ankle:brachial indices (ABI) and visible light reflectance spectroscopy hyperspectral imaging (HSI) determinations of oxygenated and deoxygenated hemoglobin (oxyHgb and deoxyHgb) levels in the skin of the distal lower extremity. This is a prospective, open, comparator trial which took place at the Vascular laboratory of a Veterans Administration Hospital in Long Beach, USA. Fifty-eight patients (85 limbs) were referred for routine vascular laboratory studies including ABI had concomitant HSI. Limbs with noncompressible pedal signals were excluded from the analysis. ABI was determined with continuous wave Doppler ultrasound and leg blood pressure cuffs. A commercial HSI system (Oxu-Vu(R), Hypermed, Inc.) was used to measure oxyHgb, deoxyHgb, and percent oxygenated hemoglobin (%oxyHgb) in the dorsum of the foot and ankle. HSI measurements of volar forearm skin were also obtained to normalize the lower extremity HSI measurements in a manner comparable with ABI. For purposes of comparison, data sets were divided into 3 groups: ABI > 0.9 (n = 53), 0.45 failed to show a clinically useful correlation between HSI measurements of oxyHgb levels, further evaluation of this novel technology is warranted. Published by Elsevier Inc.
Jeroen van Roy
Full Text Available Nowadays, quality inspection of fruit and vegetables is typically accomplished through visual inspection. Automation of this inspection is desirable to make it more objective. For this, hyperspectral imaging has been identified as a promising technique. When the field of view includes multiple objects, hypercubes should be segmented to assign individual pixels to different objects. Unsupervised and supervised methods have been proposed. While the latter are labour intensive as they require masking of the training images, the former are too computationally intensive for in-line use and may provide different results for different hypercubes. Therefore, a semi-supervised method is proposed to train a computationally efficient segmentation algorithm with minimal human interaction. As a first step, an unsupervised classification model is used to cluster spectra in similar groups. In the second step, a pixel selection algorithm applied to the output of the unsupervised classification is used to build a supervised model which is fast enough for in-line use. To evaluate this approach, it is applied to hypercubes of vine tomatoes and table grapes. After first derivative spectral preprocessing to remove intensity variation due to curvature and gloss effects, the unsupervised models segmented 86.11% of the vine tomato images correctly. Considering overall accuracy, sensitivity, specificity and time needed to segment one hypercube, partial least squares discriminant analysis (PLS-DA was found to be the best choice for in-line use, when using one training image. By adding a second image, the segmentation results improved considerably, yielding an overall accuracy of 96.95% for segmentation of vine tomatoes and 98.52% for segmentation of table grapes, demonstrating the added value of the learning phase in the algorithm.
S. T. Seydi
Full Text Available The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.
McCoppin, Ryan; Rizki, Mateen
This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.
Zheng, Yan; Bai, Jiarui; Xu, Jingna; Li, Xiayang; Zhang, Yimin
Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed. The model just contains a simple equation, center of mass coordinates, and radial distance, with which it is easy to develop classification and sorting software. A hyperspectral imaging system (HIS) with the identification model verified its practical application by using the unknown plastics. Results showed that the identification accuracy of unknown samples is 100%. All results suggested that the discrimination model was potential to an on-line characterization and sorting platform of waste plastics based on HIS. Copyright © 2017 Elsevier Ltd. All rights reserved.
Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding
Moody, Daniela; Wohlberg, Brendt
An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.
Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin
Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.
Nguyen, Lam K.; Margalith, Eli
We present a novel hyperspectral imaging technique based on tunable laser technology. By replacing the broadband source and tunable filters of a typical NIR imaging instrument, several advantages are realized, including: high spectral resolution, highly variable field-of-views, fast scan-rates, high signal-to-noise ratio, and the ability to use optical fiber for efficient and flexible sample illumination. With this technique, high-resolution, calibrated hyperspectral images over the NIR range can be acquired in seconds. The performance of system features will be demonstrated on two example applications: detecting melamine contamination in wheat gluten and separating bovine protein from wheat protein in cattle feed.
In this study, we develop a viability evaluation method for pepper (Capsicum annuum L.) seed 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 illumin...
Driver, Richard D.; Bannon, David P.; Ciccone, Domenic; Hill, Sam L.
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.
Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.
Ariana, D.; Lu, R.; Guyer, D.
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.
Full Text Available The presence of polymeric packaging film in images of food products may modify spectra obtained in hyperspectral imaging (HSI experiments, leading to undesirable image artefacts which may impede image classification. Some pre-processing of the image is typically required to reduce the presence of such artefacts. The objective of this research was to investigate the use of spectral pre-processing techniques to compensate for the presence of packaging film in hyperspectral images obtained in the visible–near infrared wavelength range (445–945 nm, with application in food quality assessment. A selection of commonly used pre-processing methods, used individually and in combination, were applied to hyperspectral images of flat homogeneous samples, imaged in the presence and absence of different packaging films (polyvinyl chloride and polyethylene terephthalate. Effects of the selected pre-treatments on variation due to the film’s presence were examined in principal components score space. The results show that the combination of first derivative Savitzky–Golay followed by standard normal variate transformation was useful in reducing variations in spectral response caused by the presence of packaging film. Compared to other methods examined, this combination has the benefits of being computationally fast and not requiring a priori knowledge about the sample or film used.
Full Text Available A low-complexity compression algorithm for hyperspectral images based on distributed source coding (DSC is proposed in this paper. The proposed distributed compression algorithm can realize both lossless and lossy compression, which is implemented by performing scalar quantization strategy on the original hyperspectral images followed by distributed lossless compression. Multilinear regression model is introduced for distributed lossless compression in order to improve the quality of side information. Optimal quantized step is determined according to the restriction of the correct DSC decoding, which makes the proposed algorithm achieve near lossless compression. Moreover, an effective rate distortion algorithm is introduced for the proposed algorithm to achieve low bit rate. Experimental results show that the compression performance of the proposed algorithm is competitive with that of the state-of-the-art compression algorithms for hyperspectral images.
Mutanga, O.; Kumar, L.
We tested the utility of imaging spectroscopy and neural networks to map phosphorus concentration in savanna grass using airborne HyMAP image data. We also sought to ascertain the key wavelengths for phosphorus prediction using hyperspectral remote sensing. The remote sensing of foliar phosphorus
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...
S. Sharifi hashjin
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.
Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo
The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and
Kim, Do-Hyun; Kim, Moon S.; Hwang, Jeeseong
Contamination of the inner surface of indwelling (implanted) medical devices by microbial biofilm is a serious problem. Some microbial bacteria such as Escherichia coli form biofilms that lead to potentially lifethreatening infections. Other types of medical devices such as bronchoscopes and duodenoscopes account for the highest number of reported endoscopic infections where microbial biofilm is one of the major causes for these infections. We applied a hyperspectral imaging method to detect biofilm contamination on the surface of several common materials used for medical devices. Such materials include stainless steel, titanium, and stainless-steeltitanium alloy. Potential uses of hyperspectral imaging technique to monitor biofilm attachment to different material surfaces are discussed.
da Silva Junior, Carlos Antonio; Nanni, Marcos Rafael; Shakir, Muhammad; Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; Cezar, Everson; de Gois, Givanildo; Lima, Mendelson; Wojciechowski, Julio Cesar; Shiratsuchi, Luciano Shozo
Infrared region of electromagnetic spectrum has remarkable applications in crop studies. Infrared along with Red band has been used to develop certain vegetation indices. These indices like NDVI, EVI provide important information on any crop physiological stages. The main objective of this research was to discriminate 4 different soybean varieties (BMX Potência, NA5909, FT Campo Mourão and Don Mario) using non-imaging hyperspectral sensor. The study was conducted in four agricultural areas in the municipality of Deodápolis (MS), Brazil. For spectral analysis, 2400 field samples were taken from soybean leaves by means of FieldSpec 3 JR spectroradiometer in the range from 350 to 2500 nm. The data were evaluated through multivariate analysis with the whole set of spectral curves isolated by blue, green, red and near infrared wavelengths along with the addition of vegetation indices like (Enhanced Vegetation Index - EVI, Normalized Difference Vegetation Index - NDVI, Green Normalized Difference Vegetation Index - GNDVI, Soil-adjusted Vegetation Index - SAVI, Transformed Vegetation Index - TVI and Optimized Soil-Adjusted Vegetation Index - OSAVI). A number of the analysis performed where, discriminant (60 and 80% of the data), simulated discriminant (40 and 20% of data), principal component (PC) and cluster analysis (CA). Discriminant and simulated discriminant analyze presented satisfactory results, with average global hit rates of 99.28 and 98.77%, respectively. The results obtained by PC and CA revealed considerable associations between the evaluated variables and the varieties, which indicated that each variety has a variable that discriminates it more effectively in relation to the others. There was great variation in the sample size (number of leaves) for estimating the mean of variables. However, it was possible to observe that 200 leaves allow to obtain a maximum error of 2% in relation to the mean.
Full Text Available In this paper, thermal (8-13 µm and hyperspectral imaging in visible and near infrared (VNIR and short wavelength infrared (SWIR ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola and non-host (Alternaria dauci pathogens to oilseed rape (Brassica napus L.. The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm, significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5% and Alternaria species (prediction accuracy 80.5%.
Milton O. Smith; Nolan J. Hess; Stephen Gulick; Lori G. Eckhardt; Roger D. Menard
This project evaluates the effectiveness of aerial hyperspectral digital imagery in the assessment of forest health of loblolly stands in central Alabama. The imagery covers 50 square miles, in Bibb and Hale Counties, south of Tuscaloosa, AL, which includes intensive managed forest industry sites and National Forest lands with multiple use objectives. Loblolly stands...
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.
Di Napoli, Claudia; Pope, Iestyn; Masia, Francesco; Watson, Peter; Langbein, Wolfgang; Borri, Paola
In this work, we demonstrate the applicability of coherent anti-Stokes Raman scattering (CARS) micro-spectroscopy for quantitative chemical imaging of saturated and unsaturated lipids in human stem-cell derived adipocytes. We compare dual-frequency/differential CARS (D-CARS), which enables rapid imaging and simple data analysis, with broadband hyperspectral CARS microscopy analyzed using an unsupervised phase-retrieval and factorization method recently developed by us for quantitative chemical image analysis. Measurements were taken in the vibrational fingerprint region (1200–2000/cm) and in the CH stretch region (2600–3300/cm) using a home-built CARS set-up which enables hyperspectral imaging with 10/cm resolution via spectral focussing from a single broadband 5 fs Ti:Sa laser source. Through a ratiometric analysis, both D-CARS and phase-retrieved hyperspectral CARS determine the concentration of unsaturated lipids with comparable accuracy in the fingerprint region, while in the CH stretch region D-CARS provides only a qualitative contrast owing to its non-linear behavior. When analyzing hyperspectral CARS images using the blind factorization into susceptibilities and concentrations of chemical components recently demonstrated by us, we are able to determine vol:vol concentrations of different lipid components and spatially resolve inhomogeneities in lipid composition with superior accuracy compared to state-of-the art ratiometric methods. PMID:24877002
In this thesis the Support Vector Machine (SVM)is applied on classification of high resolution satellite images. Sveral different measures for classification, including texture mesasures, 1st order statistics, and simple contextual information were evaluated. Additionnally, the image was segmented, using an enhanced watershed method, in order to improve the classification accuracy.
Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin
Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model
Peller, Joseph; Thompson, Kyle J.; Siddiqui, Imran; Martinie, John; Iannitti, David A.; Trammell, Susan R.
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.
India's first lunar mission, Chandrayaan-1, will have a Hyper-Spectral Imager in the visible and near-infrared spectral ... mapping of the Moon's crust in a large number of spectral channels. The planned .... In-flight verification may be done.
A multichannel hyperspectral imaging probe with 30 optic fibers covering the wavelength range of 550-1,650 nm and the light source-detector distances of 1.5-36 mm was recently developed for optical property measurement and quality evaluation of food products with flat or curved surface. This paper r...
Nondestructive methods based on fluorescence hyperspectral imaging (HSI) techniques were developed in order to detect worms on fresh-cut lettuce. The optimal wavebands for detecting worms on fresh-cut lettuce were investigated using the one-way ANOVA analysis and correlation analysis. The worm detec...
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...
Visible and near-infrared (VNIR) hyperspectral imaging (400–900 nm) was used to evaluate pH of fresh chicken breast fillets (pectoralis major muscle) from the bone (dorsal) side of individual fillets. After the principal component analysis (PCA), a band threshold method was applied to the first prin...
Kjær, Anders; Nielsen, Glenn; Stærke, Søren
The purpose of the study was to investigate the use of hyperspectral imaging (HSI) to detect and quantify chlorophyll (Chl) and total glycoalkaloid concentrations (TGA) in potatoes. To create a set of tubers with different concentrations of Chl and TGA, potatoes of four varieties were wounded...
Accurate assessment of crop disease severities is the key for precision application of pesticides to prevent disease infestation. In-situ hyperspectral imaging technology can provide high-resolution imagery with spectra for rapid identification of crop disease and determining disease infestation pat...
We used a portable hyperspectral fluorescence imaging system to evaluate biofilm formations on four types of food processing surface materials including stainless steel, polypropylene used for cutting boards, and household counter top materials such as formica and granite. The objective of this inve...
Hyperspectral fluorescence imaging techniques were investigated for detection of two genera of microbial biofilms on stainless steel material which is commonly used to manufacture food processing equipment. Stainless steel coupons were deposited in nonpathogenic E. coli O157:H7 and Salmonella cultu...
A system to take high-resolution VIS/NIR hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm), respectively, for illumination was designed, built, and tested. Components of the system include a semi-autonomous cart, a gated-intensified...
The hyperspectral microscope imaging (HMI) method can reduce detection time within 8 hours including incubation process. The early and rapid detection with this method in conjunction with the high throughput capabilities makes HMI method a prime candidate for implementation for the food industry. Th...
Goto, Atsushi; Nishikawa, Jun; Kiyotoki, Shu; Nakamura, Munetaka; Nishimura, Junichi; Okamoto, Takeshi; Ogihara, Hiroyuki; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao
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.
Hyperspectral microscope imaging (HMI) method, which provides both spatial and spectral characteristics of samples, can be effective for foodborne pathogen detection. The acousto-optic tunable filter (AOTF)-based HMI method can be used to characterize spectral properties of biofilms formed by Salmon...
Full Text Available The curse of dimensionality resulted from insufficient training samples and redundancy is considered as an important problem in the supervised classification of hyperspectral data. This problem can be handled by Feature Subset Selection (FSS methods and Support Vector Machine (SVM. The FSS methods can manage the redundancy by removing redundant spectral bands. Moreover, kernel based methods, especially SVM have a high ability to classify limited-sample data sets. This paper mainly aims to assess the capability of a FSS method and the SVM in curse of dimensional circumstances and to compare results with the Artificial Neural Network (ANN, when they are used to classify alteration zones of the Hyperion hyperspectral image acquired from the greatest Iranian porphyry copper complex. The results demonstrated that by decreasing training samples, the accuracy of SVM was just decreased 1.8% while the accuracy of ANN was highly reduced i.e. 14.01%. In addition, a hybrid FSS was applied to reduce the dimension of Hyperion. Accordingly, among the 165 useable spectral bands of Hyperion, 18 bands were only selected as the most important and informative bands. Although this dimensionality reduction could not intensively improve the performance of SVM, ANN revealed a significant improvement in the computational time and a slightly enhancement in the average accuracy. Therefore, SVM as a low-sensitive method respect to the size of training data set and feature space can be applied to classify the curse of dimensional problems. Also, the FSS methods can improve the performance of non-kernel based classifiers by eliminating redundant features. Keywords: Curse of dimensionality, Feature Subset Selection, Hydrothermal alteration, Hyperspectral, SVM
Haralick, R. M.; Dinstein, I.; Shanmugam, K.
Description of some easily computable textural features based on gray-tone spatial dependances, and illustration of their application in category-identification tasks of three different kinds of image data - namely, photomicrographs of five kinds of sandstones, 1:20,000 panchromatic aerial photographs of eight land-use categories, and ERTS multispectral imagery containing several land-use categories. Two kinds of decision rules are used - one for which the decision regions are convex polyhedra (a piecewise-linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89% for the photomicrographs, 82% for the aerial photographic imagery, and 83% for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
Vilmundardóttir, Olga K.; Sigurmundsson, Friðþór S.; Pedersen, Gro B. M.; Falco, Nicola; Rustowicz, Rose; Gísladóttir, Guðrún; Benediktsson, Jón A.
Hekla, one of the most active volcanoes in Iceland, has created a diverse volcanic landscape with lava flows, hyaloclastite and tephra fields. The variety of geological formations and different times of formation create diverse vegetation within Hekla's vicinity. The region is subjected to extensive loss of vegetation cover and soil erosion due to human utilization of woodlands and ongoing sheep grazing. The eolian activity and frequent tephra deposition has created vast areas of sparse vegetation cover. Over the 20th century, many activities have centered on preventing further loss of vegetated land and restoring ecosystems. The benefit of these activities is now noticeable in the increased vegetation and woodland cover although erosion is still active within the area. For mapping and monitoring this highly dynamic environment remote sensing techniques are extremely useful. One of the principal goals of the project 'Environmental Mapping and Monitoring of Iceland with Remote Sensing' (EMMIRS) is to use hyperspectral images and LiDAR data to classify and map the vegetation within the Hekla area. The data was collected in an aerial survey in summer 2015 by the Natural Environment Research Council (NERC), UK. The habitat type classification, currently being developed at the Icelandic Institute of Natural History and follows the structure of the EUNIS classification system, will be used for classifying the vegetation. The habitat map created by this new technique's outcome will be compared to the existent vegetation maps made by the conventional vegetation mapping method and the multispectral image classification techniques. In the field, vegetation cover, soil properties and spectral reflectance were measured within different habitat types. Special emphasis was on collecting data on vegetation and soil in the historical lavas from Hekla for assessing habitats forming over the millennia. A lava-chronosequence was established by measuring vegetation and soil in lavas
Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben
In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...
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.
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.
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.
Mizutani, Yasuhiro; Shibuya, Kyuki; Taguchi, Hiroki; Iwata, Tetsuo; Takaya, Yasuhiro; Yasui, Takeshi
In this paper, we report on comparisons of single-pixel imagings using Hadamard Transform (HT) and the ghost imaging (GI) in the view point of the visibility under weak light conditions. For comparing the two methods, we have discussed about qualities of images based on experimental results and numerical analysis. To detect images by the TH method, we have illuminated the Hadamard-pattern mask and calculated by orthogonal transform. On the other hand, the GH method can detect images by illuminating random patterns and a correlation measurement. For comparing two methods under weak light intensity, we have controlled illuminated intensities of a DMD projector about 0.1 in signal-to-noise ratio. Though a process speed of the HT image was faster then an image via the GI, the GI method has an advantage of detection under weak light condition. An essential difference between the HT and the GI method is discussed about reconstruction process. Finally, we also show a typical application of the single-pixel imaging such as hyperspectral images by using dual-optical frequency combs. An optical setup consists of two fiber lasers, spatial light modulated for generating patten illumination, and a single pixel detector. We are successful to detect hyperspectrul images in a range from 1545 to 1555 nm at 0.01nm resolution.
Tidal marshes are highly productive ecosystems that support migratory birds as roosting and over-wintering habitats on the Pacific Flyway. Microphytobenthos, or more commonly 'biofilms' contribute significantly to the primary productivity of wetland ecosystems, and provide a substantial food source for macroinvertebrates and avian communities. In this study, biofilms were characterized based on taxonomic classification, density differences, and spectral signatures. These techniques were then applied to remotely sensed images to map biofilm densities and distributions in the South Bay Salt Ponds and predict the carrying capacity of these newly restored ponds for migratory birds. The GER-1500 spectroradiometer was used to obtain in situ spectral signatures for each density-class of biofilm. The spectral variation and taxonomic classification between high, medium, and low density biofilm cover types was mapped using in-situ spectral measurements and classification of EO-1 Hyperion and Landsat TM 5 images. Biofilm samples were also collected in the field to perform laboratory analyses including chlorophyll-a, taxonomic classification, and energy content. Comparison of the spectral signatures between the three density groups shows distinct variations useful for classification. Also, analysis of chlorophyll-a concentrations show statistically significant differences between each density group, using the Tukey-Kramer test at an alpha level of 0.05. The potential carrying capacity in South Bay Salt Ponds is estimated to be 250,000 birds.
Friedel, Michael; Buscema, Massimo
A training scheme is proposed for the real-time classification of soil and vegetation (landscape) components in EO-1 Hyperion hyperspectral images. First, an auto-contractive map is used to compute connectivity of reflectance values for spectral bands (N=200) from independent laboratory spectral library components. Second, a minimum spanning tree is used to identify optimal grouping of training components from connectivity values. Third, the reflectance values for optimal landscape component signatures are sorted. Fourth, empirical distribution functions (EDF) are computed for each landscape component. Fifth, the Monte-Carlo technique is used to generate realizations (N=30) for each landscape EDF. The correspondence of component realizations to original signatures validates the stochastic procedure. Presentation of the realizations to the self-organizing map (SOM) is done using three different map sizes: 14x10, 28x20, and 40 x 30. In each case, the SOM training proceeds first with a rough phase (20 iterations using a Gaussian neighborhood with an initial and final radius of 11 units and 3 units) and then fine phase (400 iterations using a Gaussian neighborhood with an initial and final radius of 3 units and 1 unit). The initial and final learning rates of 0.5 and 0.05 decay linearly down to 10-5, and the Gaussian neighborhood function decreases exponentially (decay rate of 10-3 iteration-1) providing reasonable convergence. Following training of the three networks, each corresponding SOM is used to independently classify the original spectral library signatures. In comparing the different SOM networks, the 28x20 map size is chosen for independent reproducibility and processing speed. The corresponding universal distance matrix reveals separation of the seven component classes for this map size thereby supporting it use as a Hyperion classifier.
Full Text Available The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN method, which is a batch analysis. Hyperspectral imaging (HSI can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR region (~900–2500 nm and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel; ii calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 s; RMSEP ~ 63 s compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variate showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA was also applied to segregate wheat kernels into low (250 s HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using an HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production.
Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai
In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.
Shiozawa, Manabu; Shirai, Masataka; Izumisawa, Junko; Tanabe, Maiko; Watanabe, Koichi
Non-invasive cell analyses are increasingly important for medical field. A CARS microscope is one of the non-invasive imaging equipments and enables to obtain images indicating molecular distribution. Some studies on discrimination of cell state by using CARS images of lipid are reported. However, due to low signal intensity, it is still challenging to obtain images of the fingerprint region (800~1800 cm-1), in which many spectrum peaks correspond to compositions of a cell. Here, to identify cell differentiation by using multiplex CARS, we investigated hyperspectral imaging of fingerprint region of living cells. To perform multiplex CARS, we used a prototype of a compact light source, which consists of a microchip laser, a single-mode fiber, and a photonic crystal fiber to generate supercontinuum light. Assuming application to regenerative medicine, we chose a cartilage cell, whose differentiation is difficult to be identified by change of the cell morphology. Because one of the major components of cartilage is collagen, we focused on distribution of proline, which accounts for approximately 20% of collagen in general. The spectrum quality was improved by optical adjustments about power branching ratio and divergence of broadband Stokes light. Hyperspectral images were successfully obtained by the improvement. Periphery of a cartilage cell was highlighted in CARS image of proline, and this result suggests correspondence with collagen generated as extracellular matrix. A possibility of cell analyses by using CARS hyperspectral imaging was indicated.
Honkavaara, Eija; Rosnell, Tomi; Oliveira, Raquel; Tommaselli, Antonio
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
Full Text Available Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.
Sun, Meijun; Zhang, Dong; Wang, Zheng; Ren, Jinchang; Chai, Bolong; Sun, Jizhou
Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise.
Majda, Alicja; Wietecha-Posłuszny, Renata; Mendys, Agata; Wójtowicz, Anna; Łydżba-Kopczyńska, Barbara
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.
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.
The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring...
Levaux, Florian; Bostater, Charles R., Jr.; Neyt, Xavier
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
Full Text Available Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.
Klerk, L.A.; Broersen, A.; Fletcher, I.W.; Liere, van R.; Heeren, R.M.A.
The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable
Yi, Weisong; Zhang, Jian; Jiang, Houmin; Zhang, Niya
Gastric cancer is one of the leading causes of cancer death in the world due to its high morbidity and mortality. Hyperspectral imaging (HSI) is an emerging, non-destructive, cutting edge analytical technology that combines conventional imaging and spectroscopy in one single system. The manuscript has investigated the application of near-infrared hyperspectral imaging (900-1700 nm) (NIR-HSI) for gastric cancer detection with algorithms. Major spectral differences were observed in three regions (950-1050, 1150-1250, and 1400-1500 nm). By inspecting cancerous mean spectrum three major absorption bands were observed around 975, 1215 and 1450 nm. Furthermore, the cancer target detection results are consistent and conformed with histopathological examination results. These results suggest that NIR-HSI is a simple, feasible and sensitive optical diagnostic technology for gastric cancer target detection with chemometrics.
Full Text Available Hyperspectral imaging systems used in plant science or agriculture often have suboptimal signal-to-noise ratio in the blue region (400–500 nm of the electromagnetic spectrum. Typically there are two principal reasons for this effect, the low sensitivity of the imaging sensor and the low amount of light available from the illuminating source. In plant science, the blue region contains relevant information about the physiology and the health status of a plant. We report on the improvement in sensitivity of a hyperspectral imaging system in the blue region of the spectrum by using supplemental illumination provided by an array of high brightness light emitting diodes (LEDs with an emission peak at 470 nm.
Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman
The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.
Zhang, Dong-Yan; Zhang, Jing-Cheng; Zhu, Da-Zhou; Wang, Ji-Hua; Luo, Ju-Hua; Zhao, Jin-Ling; Huang, Wen-Jiang
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.
Markelin, L.; Honkavaara, E.; Näsi, R.; Viljanen, N.; Rosnell, T.; Hakala, T.; Vastaranta, M.; Koivisto, T.; Holopainen, M.
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.
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.
What image classification does is to assign pixel to a particular land cover and land use type that has the most similar spectral signature. However, there are possibilities that different methods or algorithms of image classification of the same data set could produce appreciable variant results in the sizes, shapes and areas of ...
Klomkaew, Phiwat; Mayes, Sam A.; Rich, Thomas C.; Leavesley, Silas J.
Our lab has worked to develop high-speed hyperspectral imaging systems that scan the fluorescence excitation spectrum for biomedical imaging applications. Hyperspectral imaging can be used in remote sensing, medical imaging, reaction analysis, and other applications. Here, we describe the development of a hyperspectral imaging system that comprised an inverted Nikon Eclipse microscope, sCMOS camera, and a custom light source that utilized a series of high-power LEDs. LED selection was performed to achieve wavelengths of 350-590 nm. To reduce scattering, LEDs with low viewing angles were selected. LEDs were surface-mount soldered and powered by an RCD. We utilized 3D printed mounting brackets to assemble all circuit components. Spectraradiometric calibration was performed using a spectrometer (QE65000, Ocean Optics) and integrating sphere (FOIS-1, Ocean Optics). Optical output and LED driving current were measured over a range of illumination intensities. A normalization algorithm was used to calibrate and optimize the intensity of the light source. The highest illumination power was at 375 nm (3300 mW/cm2), while the lowest illumination power was at 515, 525, and 590 nm (5200 mW/cm2). Comparing the intensities supplied by each LED to the intensities measured at the microscope stage, we found there was a great loss in power output. Future work will focus on using two of the same LEDs to double the power and finding more LED and/or laser diodes and chips around the range. This custom hyperspectral imaging system could be used for the detection of cancer and the identification of biomolecules.
N. N. Imai
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.
Full Text Available Visible/near-infrared (Vis/NIR hyperspectral imaging (400–1000 nm was applied to identify the growth process of Aspergillus flavus and Aspergillus parasiticus. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated A. flavus and A. parasiticus on day 1 from other days. Image at band of 520 nm classified A. parasiticus on day 6. Principle component analysis (PCA was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC2 to PC6 gave a rough clustering of fungi in the same incubation time. However, in the plot, A. flavus on day 3 and day 4 and A. parasiticus on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM classifier were applied to the full spectral range. SVM models built by PC2 to PC6 could identify fungal growth days with accuracies of 92.59% and 100% for A. flavus and A. parasiticus individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm and seven (461, 538, 542, 742, 753, 756, 919 nm wavelengths were selected for A. flavus and A. parasiticus, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for A. flavus and A. parasiticus, respectively. Finally, the visualized prediction images for A. flavus and A. parasiticus in different growth days were made by applying the optimal wavelength’s SVM models on every pixel of the hyperspectral image.
Full Text Available The detection of water stress in vineyards plays an integral role in the sustainability of high-quality grapes and prevention of devastating crop loses. Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress. In this study, we applied two ensemble learners, i.e., random forest (RF and extreme gradient boosting (XGBoost, for discriminating stressed and non-stressed Shiraz vines using terrestrial hyperspectral imaging. Additionally, we evaluated the utility of a spectral subset of wavebands, derived using RF mean decrease accuracy (MDA and XGBoost gain. Our results show that both ensemble learners can effectively analyse the hyperspectral data. When using all wavebands (p = 176, RF produced a test accuracy of 83.3% (KHAT (kappa analysis = 0.67, and XGBoost a test accuracy of 80.0% (KHAT = 0.6. Using the subset of wavebands (p = 18 produced slight increases in accuracy ranging from 1.7% to 5.5% for both RF and XGBoost. We further investigated the effect of smoothing the spectral data using the Savitzky-Golay filter. The results indicated that the Savitzky-Golay filter reduced model accuracies (ranging from 0.7% to 3.3%. The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi-automated framework for vineyard water stress modelling.
Full Text Available spatially induced disjoint classes whose neighborhood relations are difficult to capture using traditional graph based embedding techniques. Robust parameter estimation is a challenge in traditional kernel functions that compute neighborhood graphs e...
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
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.
Medina, José M.; Pereira, Luís M.; Correia, Hélder T.; Nascimento, Sérgio M. C.
We report a hyperspectral imaging system to measure the reflectance spectra of real human irises with high spatial resolution. A set of ocular prosthesis was used as the control condition. Reflectance data were decorrelated by the principal-component analysis. The main conclusion is that spectral complexity of the human iris is considerable: between 9 and 11 principal components are necessary to account for 99% of the cumulative variance in human irises. Correcting image misalignments associated with spontaneous ocular movements did not influence this result. The data also suggests a correlation between the first principal component and different levels of melanin present in the irises. It was also found that although the spectral characteristics of the first five principal components were not affected by the radial and angular position of the selected iridal areas, they affect the higher-order ones, suggesting a possible influence of the iris texture. The results show that hyperspectral imaging in the iris, together with adequate spectroscopic analyses provide more information than conventional colorimetric methods, making hyperspectral imaging suitable for the characterization of melanin and the noninvasive diagnosis of ocular diseases and iris color.
Zuzak, Karel J.; Wehner, Eleanor; Rao, Shekar; Litorja, Maritoni; Allen, David W.; Singer, Mike; Purdue, Gary; Ufret-Vincenty, Rafael; White, Jonathan; Cadeddu, Jeffrey; Livingston, Edward
Utilizing seed funding from Texas Instruments, a DLP (R)Hyperspectral Imaging system was developed by integrating a focal-plane array, FPA, detector with a DLP based spectrally tunable illumination source. Software is used to synchronize FPA with DLP hardware for collecting spectroscopic images as well as running novel illumination schemes and chemometric deconvolution methods for producing gray scale or color encoded images visualizing molecular constituents at video rate. Optical spectra and spectroscopic image data of a variety of live human organs and diseased tissue collected from patients during surgical procedures and clinical visits being cataloged for a database will be presented.
Synchrotron-based high spatial resolution hyperspectral infrared imaging technique provides thousands of infrared spectra with high resolution, thus allowing us to acquire detailed spatial maps of chemical molecular structures for many grains in short times. Utilizing this technique, thousands of infrared spectra were analyzed at once instead of inspecting each spectrum separately. Sutter's Mill meteorite is a unique carbonaceous type meteorite with highly heterogeneous chemical composition. Multiple grains from the Sutter's Mill meteorite have been studied using this technique and the presence of both hydrous and anhydrous silicate minerals have been observed. It is observed that the carbonate mineralogy varies from simple to more complex carbonates even within a few microns in the meteorite grains. These variations, the type and distribution of calcite-like vs. dolomite-like carbonates are presented by means of hyperspectral FTIR imaging spectroscopy with high resolution. Various scenarios for the formation of different carbonate compositions in the Sutter's Mill parent body are discussed.
Sun, Ye; Wang, Yihang; Xiao, Hui; Gu, Xinzhe; Pan, Leiqing; Tu, Kang
Honey peach is a very common but highly perishable market fruit. When pathogens infect fruit, chlorophyll as one of the important components related to fruit quality, decreased significantly. Here, the feasibility of hyperspectral imaging to determine the chlorophyll content thus distinguishing diseased peaches was investigated. Three optimal wavelengths (617nm, 675nm, and 818nm) were selected according to chlorophyll content via successive projections algorithm. Partial least square regression models were established to determine chlorophyll content. Three band ratios were obtained using these optimal wavelengths, which improved spatial details, but also integrates the information of chemical composition from spectral characteristics. The band ratio values were suitable to classify the diseased peaches with 98.75% accuracy and clearly show the spatial distribution of diseased parts. This study provides a new perspective for the selection of optimal wavelengths of hyperspectral imaging via chlorophyll content, thus enabling the detection of fungal diseases in peaches. Copyright © 2017 Elsevier Ltd. All rights reserved.
Fei, Baowei; Lu, Guolan; Wang, Xu; Zhang, Hongzheng; Little, James V.; Patel, Mihir R.; Griffith, Christopher C.; El-Diery, Mark W.; Chen, Amy Y.
A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.
Lee, Hoonsoo; Kim, Moon S.; Qin, Jianwei; Park, Eunsoo; Song, Yu-Rim; Oh, Chang-Sik; Cho, Byoung-Kwan
The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive det...
Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An
There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.
Baca-Bocanegra, Berta; Nogales-Bueno, Julio; Hernández-Hierro, José Miguel; Heredia, Francisco José
Extractable total phenolic content of American non-toasted oak (Quercus alba L.) shavings has been determined using near infrared hyperspectral imaging. A like-wine model solution was used for the simulated maceration procedure. Calibrations were performed by partial least squares regression (MPLS) using a number of spectral pre-treatments. The coefficient of determination of wood for extractable total phenolic content was 0.89, and the standard error of prediction was 6.3 mg g -1 . Thus, near infrared hyperspectral imaging arises as an attractive strategy for predicting extractable total phenolic content in the range of 0-65 mg g -1 , of great relevance from the point of view of quality assurance regarding wood used in the wine sector. Near infrared hyperspectral imaging arises as an attractive strategy for the feasibility of enhancing the value of cooperage byproduct through the fast determination of extractable bioactive molecules, such as polyphenols. Copyright © 2017 Elsevier Ltd. All rights reserved.
Ai, Yufei; Li, Jun; Shi, Wenjing; Schmit, Timothy J.; Cao, Changyong; Li, Wanbiao
Deep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal-to-noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT-2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.
Poole, Kristin M.; Patil, Chetan A.; Nelson, Christopher E.; McCormack, Devin R.; Madonna, Megan C.; Duvall, Craig L.; Skala, Melissa C.
Peripheral arterial disease (PAD) is an atherosclerotic disease of the extremities that leads to high rates of myocardial infarction and stroke, increased mortality, and reduced quality of life. PAD is especially prevalent in diabetic patients, and is commonly modeled by hind limb ischemia in mice to study collateral vessel development and test novel therapies. Current techniques used to assess recovery cannot obtain quantitative, physiological data non-invasively. Here, we have applied hyperspectral imaging and swept source optical coherence tomography (OCT) to study longitudinal changes in blood oxygenation and vascular morphology, respectively, intravitally in the diabetic mouse hind limb ischemia model. Additionally, recommended ranges for controlling physiological variability in blood oxygenation with respect to respiration rate and body core temperature were determined from a control animal experiment. In the longitudinal study with diabetic mice, hyperspectral imaging data revealed the dynamics of blood oxygenation recovery distally in the ischemic footpad. In diabetic mice, there is an early increase in oxygenation that is not sustained in the long term. Quantitative analysis of vascular morphology obtained from Hessian-filtered speckle variance OCT volumes revealed temporal dynamics in vascular density, total vessel length, and vessel diameter distribution in the adductor muscle of the ischemic limb. The combination of hyperspectral imaging and speckle variance OCT enabled acquisition of novel functional and morphological endpoints from individual animals, and provides a more robust platform for future preclinical evaluations of novel therapies for PAD.
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.
Kise, Drew P; Magana, Donny; Reddish, Michael J; Dyer, R Brian
We report a continuous-flow, microfluidic mixer utilizing mid-infrared hyperspectral imaging detection, with an experimentally determined, submillisecond mixing time. The simple and robust mixer design has the microfluidic channels cut through a polymer spacer that is sandwiched between two IR transparent windows. The mixer hydrodynamically focuses the sample stream with two side flow channels, squeezing it into a thin jet and initiating mixing through diffusion and advection. The detection system generates a mid-infrared hyperspectral absorbance image of the microfluidic sample stream. Calibration of the hyperspectral image yields the mid-IR absorbance spectrum of the sample versus time. A mixing time of 269 μs was measured for a pD jump from 3.2 to above 4.5 in a D2O sample solution of adenosine monophosphate (AMP), which acts as an infrared pD indicator. The mixer was further characterized by comparing experimental results with a simulation of the mixing of an H2O sample stream with a D2O sheath flow, showing good agreement between the two. The IR microfluidic mixer eliminates the need for fluorescence labeling of proteins with bulky, interfering dyes, because it uses the intrinsic IR absorbance of the molecules of interest, and the structural specificity of IR spectroscopy to follow specific chemical changes such as the protonation state of AMP.
Lee, Hoonsoo; Kim, Moon S; Qin, Jianwei; Park, Eunsoo; Song, Yu-Rim; Oh, Chang-Sik; Cho, Byoung-Kwan
The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400-1800 cm -1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm -1 and 437 cm -1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods.
Lohumi, Santosh; Lee, Hoonsoo; Kim, Moon S; Qin, Jianwei; Kandpal, Lalit Mohan; Bae, Hyungjin; Rahman, Anisur; Cho, Byoung-Kwan
The potential adulteration of foodstuffs has led to increasing concern regarding food safety and security, in particular for powdered food products where cheap ground materials or hazardous chemicals can be added to increase the quantity of powder or to obtain the desired aesthetic quality. Due to the resulting potential health threat to consumers, the development of a fast, label-free, and non-invasive technique for the detection of adulteration over a wide range of food products is necessary. We therefore report the development of a rapid Raman hyperspectral imaging technique for the detection of food adulteration and for authenticity analysis. The Raman hyperspectral imaging system comprises of a custom designed laser illumination system, sensing module, and a software interface. Laser illumination system generates a 785 nm laser line of high power, and the Gaussian like intensity distribution of laser beam is shaped by incorporating an engineered diffuser. The sensing module utilize Rayleigh filters, imaging spectrometer, and detector for collection of the Raman scattering signals along the laser line. A custom-built software to acquire Raman hyperspectral images which also facilitate the real time visualization of Raman chemical images of scanned samples. The developed system was employed for the simultaneous detection of Sudan dye and Congo red dye adulteration in paprika powder, and benzoyl peroxide and alloxan monohydrate adulteration in wheat flour at six different concentrations (w/w) from 0.05 to 1%. The collected Raman imaging data of the adulterated samples were analyzed to visualize and detect the adulterant concentrations by generating a binary image for each individual adulterant material. The results obtained based on the Raman chemical images of adulterants showed a strong correlation (R>0.98) between added and pixel based calculated concentration of adulterant materials. This developed Raman imaging system thus, can be considered as a powerful
Full Text Available apply our method to synthetic images, including a standard test image developed by Chein-I Chang, with good results for Gaussian independent noise. Index Terms— Hyperspectral Unmixing, Random Ma- trix Theory, Linear Mixture Model, Virtual Dimension... function, and K is the number of endmembers. We assume Gaussian noise following the methods of  . The first step in unmixing the image is to determine how many endmembers or constituents are contained in the scene. This is known as the Virtual...
A single hyperspectral imaging sensor can produce frames with spatially-continuous rows of differing, but adjacent, spectral wavelength. If the frame sample-rate of the sensor is such that subsequent hyperspectral frames are spatially shifted by one row, then the sensor can be thought of as a parallel (in wavelength) push-broom sensor. An examination of data compression techniques for such a sensor is presented. The compression techniques are intended to be implemented onboard a space-based platform and to have implementation speeds that match the date rate of the sensor. Data partitions examined extend from individually operating on a single hyperspectral frame to operating on a data cube comprising the two spatial axes and the spectral axis. Compression algorithms investigated utilize JPEG-based image compression, wavelet-based compression and differential pulse code modulation. Algorithm performance is quantitatively presented in terms of root-mean-squared error and root-mean-squared correlation coefficient error. Implementation issues are considered in algorithm development.
Chennu, Arjun; Fä rber, Paul; Volkenborn, Nils; Alnajjar, Mohammad Ahmad; Janssen, Felix; de Beer, Dirk; Polerecky, Lubos
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.
Sinjab, Faris; Sicilia, Giovanna; Shipp, Dustin W; Marlow, Maria; Notingher, Ioan
While Raman hyperspectral imaging has been widely used for label-free mapping of biomolecules in cells, these measurements require the cells to be cultured on weakly Raman scattering substrates. However, many applications in biological sciences and engineering require the cells to be cultured on polymer substrates that often generate large Raman scattering signals. Here, we discuss the theoretical limits of the signal-to-noise ratio in the Raman spectra of cells in the presence of polymer signals and how optical aberrations may affect these measurements. We show that Raman spectra of cells cultured on polymer substrates can be obtained using automatic subtraction of the polymer signals and demonstrate the capabilities of these methods in two important applications: tissue engineering and in vitro toxicology screening of drugs. Apart from their scientific and technological importance, these applications are examples of the two most common measurement configurations: (1) cells cultured on an optically thick polymer substrate measured using an immersion/dipping objective; and (2) cells cultured on a transparent polymer substrate and measured using an inverted optical microscope. In these examples, we show that Raman hyperspectral data sets with sufficient quality can be successfully acquired to map the distribution of common biomolecules in cells, such as nucleic acids, proteins, and lipids, as well as detecting the early stages of apoptosis. We also discuss strategies for further improvements that could expand the application of Raman hyperspectral imaging on polymer substrates even further in biomedical sciences and engineering.
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.
Full Text Available Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA.
Liao, Yuan-Hsun; Lo, Wei-Sheng; Guo, Horng-Yuh; Kao, Ching-Hua; Chou, Tau-Meu; Chen, Junne-Jih; Wen, Chia-Hsien; Lin, Chinsu; Chen, Hsian-Min; Ouyang, Yen-Chieh; Wu, Chao-Cheng; Chen, Shih-Yu; Chang, Chein-I.
Pesticide residue detection in agriculture crops is a challenging issue and is even more difficult to quantify pesticide residue resident in agriculture produces and fruits. This paper conducts a series of base-line experiments which are particularly designed for three specific pesticides commonly used in Taiwan. The materials used for experiments are single leaves of vegetable produces which are being contaminated by various amount of concentration of pesticides. Two sensors are used to collected data. One is Fourier Transform Infrared (FTIR) spectroscopy. The other is a hyperspectral sensor, called Geophysical and Environmental Research (GER) 2600 spectroradiometer which is a batteryoperated field portable spectroradiometer with full real-time data acquisition from 350 nm to 2500 nm. In order to quantify data with different levels of pesticide residue concentration, several measures for spectral discrimination are developed. Mores specifically, new measures for calculating relative power between two sensors are particularly designed to be able to evaluate effectiveness of each of sensors in quantifying the used pesticide residues. The experimental results show that the GER is a better sensor than FTIR in the sense of pesticide residue quantification.
Full Text Available Fungi infection in maize kernels is a major concern worldwide due to its toxic metabolites such as mycotoxins, thus it is necessary to develop appropriate techniques for early detection of fungi infection in maize kernels. Thirty-six sterilised maize kernels were inoculated each day with Aspergillus parasiticus from one to seven days, and then seven groups (D1, D2, D3, D4, D5, D6, D7 were determined based on the incubated time. Another 36 sterilised kernels without inoculation with fungi were taken as control (DC. Hyperspectral images of all kernels were acquired within spectral range of 921–2529 nm. Background, labels and bad pixels were removed using principal component analysis (PCA and masking. Separability computation for discrimination of fungal contamination levels indicated that the model based on the data of the germ region of individual kernels performed more effectively than on that of the whole kernels. Moreover, samples with a two-day interval were separable. Thus, four groups, DC, D1–2 (the group consisted of D1 and D2, D3–4 (D3 and D4, and D5–7 (D5, D6, and D7, were defined for subsequent classification. Two separate sample sets were prepared to verify the influence on a classification model caused by germ orientation, that is, germ up and the mixture of germ up and down with 1:1. Two smooth preprocessing methods (Savitzky-Golay smoothing, moving average smoothing and three scatter-correction methods (normalization, standard normal variate, and multiple scatter correction were compared, according to the performance of the classification model built by support vector machines (SVM. The best model for kernels with germ up showed the promising results with accuracies of 97.92% and 91.67% for calibration and validation data set, respectively, while accuracies of the best model for samples of the mixed kernels were 95.83% and 84.38%. Moreover, five wavelengths (1145, 1408, 1935, 2103, and 2383 nm were selected as the key
Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Farley, Vincent; Guyot, Éric; Lagueux, Philippe; Morton, Vince; Giroux, Jean; Chamberland, Martin
There are many types of natural gas fields including shale formations that are common especially in the St-Lawrence Valley (Canada). Since methane (CH4), the major component of shale gas, is odorless, colorless and highly flammable, in addition to being a greenhouse gas, methane emanations and/or leaks are important to consider for both safety and environmental reasons. Telops recently launched on the market the Hyper-Cam Methane, a field-deployable thermal infrared hyperspectral camera specially tuned for detecting methane infrared spectral features under ambient conditions and over large distances. In order to illustrate the benefits of this novel research instrument for natural gas imaging, the instrument was brought on a site where shale gas leaks unexpectedly happened during a geological survey near the Enfant-Jesus hospital in Quebec City, Canada, during December 2014. Quantitative methane imaging was carried out based on methane's unique infrared spectral signature. Optical flow analysis was also carried out on the data to estimate the methane mass flow rate. The results show how this novel technique could be used for advanced research on shale gases.
Nyström, Sofie; Bäck, Marcus; Nilsson, K Peter R; Hammarström, Per
Proteins that deposit as amyloid in tissues throughout the body can be the cause or consequence of a large number of diseases. Among these we find neurodegenerative diseases such as Alzheimer's and Parkinson's disease afflicting primarily the central nervous system, and systemic amyloidosis where serum amyloid A, transthyretin and IgG light chains deposit as amyloid in liver, carpal tunnel, spleen, kidney, heart, and other peripheral tissues. Amyloid has been known and studied for more than a century, often using amyloid specific dyes such as Congo red and Thioflavin T (ThT) or Thioflavin (ThS). In this paper, we present heptamer-formyl thiophene acetic acid (hFTAA) as an example of recently developed complements to these dyes called luminescent conjugated oligothiophenes (LCOs). hFTAA is easy to use and is compatible with co-staining in immunofluorescence or with other cellular markers. Extensive research has proven that hFTAA detects a wider range of disease associated protein aggregates than conventional amyloid dyes. In addition, hFTAA can also be applied for optical assignment of distinct aggregated morphotypes to allow studies of amyloid fibril polymorphism. While the imaging methodology applied is optional, we here demonstrate hyperspectral imaging (HIS), laser scanning confocal microscopy and fluorescence lifetime imaging (FLIM). These examples show some of the imaging techniques where LCOs can be used as tools to gain more detailed knowledge of the formation and structural properties of amyloids. An important limitation to the technique is, as for all conventional optical microscopy techniques, the requirement for microscopic size of aggregates to allow detection. Furthermore, the aggregate should comprise a repetitive β-sheet structure to allow for hFTAA binding. Excessive fixation and/or epitope exposure that modify the aggregate structure or conformation can render poor hFTAA binding and hence pose limitations to accurate imaging.
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...
Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C
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
Pandey, Piyush; Ge, Yufeng; Stoerger, Vincent; Schnable, James C.
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
Lee, Hoonsoo; Kim, Moon S; Song, Yu-Rim; Oh, Chang-Sik; Lim, Hyoun-Sub; Lee, Wang-Hee; Kang, Jum-Soon; Cho, Byoung-Kwan
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.
Saari, H.; Akujärvi, A.; Holmlund, C.; Ojanen, H.; Kaivosoja, J.; Nissinen, A.; Niemeläinen, O.
The accurate determination of the quality parameters of crops requires a spectral range from 400 nm to 2500 nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15 kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high ( 50 k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded DroneKnowledge - Towards knowledge based export of small UAS remote sensing technology project.
Jun, Won; Lee, Kangjin; Millner, Patricia; Sharma, Manan; Chao, Kuanglin; Kim, Moon S.
A rapid nondestructive technology is needed to detect bacterial contamination on the surfaces of food processing equipment to reduce public health risks. A portable hyperspectral fluorescence imaging system was used to evaluate potential detection of microbial biofilm on stainless steel typically used in the manufacture of food processing equipment. Stainless steel coupons were immersed in bacterium cultures, such as E. coli, Pseudomonas pertucinogena, Erwinia chrysanthemi, and Listeria innocula. Following a 1-week exposure, biofilm formations were assessed using fluorescence imaging. In addition, the effects on biofilm formation from both tryptic soy broth (TSB) and M9 medium with casamino acids (M9C) were examined. TSB grown cells enhance biofilm production compared with M9C-grown cells. Hyperspectral fluorescence images of the biofilm samples, in response to ultraviolet-A (320 to 400 nm) excitation, were acquired from approximately 416 to 700 nm. Visual evaluation of individual images at emission peak wavelengths in the blue revealed the most contrast between biofilms and stainless steel coupons. Two-band ratios compared with the single-band images increased the contrast between the biofilm forming area and stainless steel coupon surfaces. The 444/588 nm ratio images exhibited the greatest contrast between the biofilm formations and stainless coupon surfaces.
Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan
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.
Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan
Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.
Full Text Available and points to new directions that boost unsupervised pattern classification. In particular, the paper offers design insights on the generation of a well structured graph Laplacian based on an affinity function that induces context-dependence to create compact...
Non-destructive and rapid prediction of quality attributes of chicken breast fillets using visible and near-infrared (VNIR) hyperspectral imaging (400-1000 nm) was carried out in this work. All hyperspectral images were acquired for bone (dorsal) side of chicken breast. A forward principal component...
Sun, Lei; Khan, Shuhab D.; Sarmiento, Sergio; Lakshmikantha, M. R.; Zhou, Huawei
Petroleum geoscientists have been using cores and well logs to study source rocks and reservoirs, however, the inherent discontinuous nature of these data cannot account for horizontal heterogeneities. Modern exploitation requires better understanding of important source rocks and reservoirs at outcrop scale. Remote sensing of outcrops is becoming a first order tool for reservoir analog studies including horizontal heterogeneities. This work used ground-based hyperspectral imaging, terrestrial laser scanning (TLS), and high-resolution photography to study a roadcut of the Boone Formation at Bella Vista, northwest Arkansas, and developed an outcrop model for reservoir analog analyses. The petroliferous Boone Formation consists of fossiliferous limestones interbedded with chert of early Mississippian age. We used remote sensing techniques to identify rock types and to collect 3D geometrical data. Mixture tuned matched filtering classification of hyperspectral data show that the outcrop is mostly limestones with interbedded chert nodules. 1315 fractures were classified according to their strata-bounding relationships, among these, larger fractures are dominantly striking in ENE - WSW directions. Fracture extraction data show that chert holds more fractures than limestones, and both vertical and horizontal heterogeneities exist in chert nodule distribution. Utilizing ground-based remote sensing, we have assembled a virtual outcrop model to extract mineral composition as well as fracture data from the model. We inferred anisotropy in vertical fracture permeability based on the dominancy of fracture orientations, the preferential distribution of fractures and distribution of chert nodules. These data are beneficial in reservoir analogs to study rock mechanics and fluid flow, and to improve well performances.
Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting
Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.
Zavattini, Guido; Vecchi, Stefania; Mitchell, Gregory; Weisser, Ulli; Leahy, Richard M; Pichler, Bernd J; Smith, Desmond J; Cherry, Simon R
In vivo optical instruments designed for small animal imaging generally measure the integrated light intensity across a broad band of wavelengths, or make measurements at a small number of selected wavelengths, and primarily use any spectral information to characterize and remove autofluorescence. We have developed a flexible hyperspectral imaging instrument to explore the use of spectral information to determine the 3D source location for in vivo fluorescence imaging applications. We hypothesize that the spectral distribution of the emitted fluorescence signal can be used to provide additional information to 3D reconstruction algorithms being developed for optical tomography. To test this hypothesis, we have designed and built an in vivo hyperspectral imaging system, which can acquire data from 400 to 1000 nm with 3 nm spectral resolution and which is flexible enough to allow the testing of a wide range of illumination and detection geometries. It also has the capability to generate a surface contour map of the animal for input into the reconstruction process. In this paper, we present the design of the system, demonstrate the depth dependence of the spectral signal in phantoms and show the ability to reconstruct 3D source locations using the spectral data in a simple phantom. We also characterize the basic performance of the imaging system
Liu Zhiyi; Ma Suihua; Liu Le; Guo Jihua; He Yonghong; Ji Yanhong
Microarray research offers great potential for analysis of gene expression profile and leads to greatly improved experimental throughput. A number of instruments have been reported for microarray detection, such as chemiluminescence, surface plasmon resonance, and fluorescence markers. Fluorescence imaging is popular for the readout of microarrays. In this paper we develop a quasi-confocal, multichannel parallel scan hyperspectral fluorescence imaging system for microarray research. Hyperspectral imaging records the entire emission spectrum for every voxel within the imaged area in contrast to recording only fluorescence intensities of filter-based scanners. Coupled with data analysis, the recorded spectral information allows for quantitative identification of the contributions of multiple, spectrally overlapping fluorescent dyes and elimination of unwanted artifacts. The mechanism of quasi-confocal imaging provides a high signal-to-noise ratio, and parallel scan makes this approach a high throughput technique for microarray analysis. This system is improved with a specifically designed spectrometer which can offer a spectral resolution of 0.2 nm, and operates with spatial resolutions ranging from 2 to 30 μm . Finally, the application of the system is demonstrated by reading out microarrays for identification of bacteria.
Kiyotoki, Shu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao
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.
Lim, Hoong-Ta; Murukeshan, Vadakke Matham
Hyperspectral imaging was first used in remote sensing and since then, it has been used in many other applications such as cancer diagnosis, precision farming and assessment of the level of flaking in ancient murals. In order to make hyperspectral imaging available for a wide variety of applications, its imagers can be made to operate using different methods and developed into different configurations. This leads to each variant having a set of specifications suitable for certain applications. The many variants of hyperspectral imager produce a set of three-dimensional spatial-spatialspectral datacube, which is made up of hundreds of spectral images of one scene. A snapshot hyperspectral imaging probe has recently been developed by integrating a fiber bundle, which is made up of specially-arranged optical fibers, with a spectrograph-based hyperspectral imager. The snapshot method is able to produce a datacube using the information from each scan. The fiber bundle has 100 fiberlets which are arranged in a row in the one-dimensional proximal end, and are rearranged into a 10×10 hexagonal array in the two-dimensional distal end. The image captured by the two-dimensional end of the fiber bundle is reduced from two to one spatial dimension at the one-dimensional end. The raw data acquired from each scan has to be remapped into a datacube with the correct representation of the spectral and spatial features of the captured scene. This paper reports the spatial calibrations of both ends of the fiber bundle and image processing that have to be performed for such a remapping.
Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær
A two-stage hierarchical classification scheme of psoriasis lesion images is proposed. These images are basically composed of three classes: normal skin, lesion and background. The scheme combines conventional tools to separate the skin from the background in the first stage, and the lesion from...
A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested, In the classification strategy, a p...
Choi, Heejin; Wadduwage, Dushan; Matsudaira, Paul T.; So, Peter T.C.
A depth resolved hyperspectral imaging spectrometer can provide depth resolved imaging both in the spatial and the spectral domain. Images acquired through a standard imaging Fourier transform spectrometer do not have the depth-resolution. By post processing the spectral cubes (x, y, λ) obtained through a Sagnac interferometer under uniform illumination and structured illumination, spectrally resolved images with depth resolution can be recovered using structured light illumination algorithms such as the HiLo method. The proposed scheme is validated with in vitro specimens including fluorescent solution and fluorescent beads with known spectra. The system is further demonstrated in quantifying spectra from 3D resolved features in biological specimens. The system has demonstrated depth resolution of 1.8 μm and spectral resolution of 7 nm respectively. PMID:25360367
Multispectral imaging algorithms were developed using visible-near-infrared (VNIR) and near-infrared (NIR) hyperspectral imaging (HSI) techniques to detect worms on fresh-cut lettuce. The optimal wavebands that detect worm on fresh-cut lettuce for each type of HSI were investigated using the one-way...
Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spina...
Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai
With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.
Morton, V.; Gagnon, M. A.; Marcotte, F.; Gouhier, M.; Smekens, J. F.
Many urban areas are located near active volcanoes around the world. Therefore, scientific research on different indicators of imminent eruptions is carried out on an ongoing basis. Due to the hazardous and unpredictable behavior of volcanoes, remote sensing technologies are normally preferred for investigations. Over the years, the Telops Hyper-Cam, a high-performance infrared hyperspectral camera, has established itself as a reference tool for investigating gas clouds over large distances. In order to illustrate the benefits of standoff infrared hyperspectral imaging for characterizing volcanic processes, many different measurements were carried out from an elevated point ( 800 m) of the Stromboli volcano (Italy) by researchers from the Université Blaise-Pascal (Clermont-Ferrand, France). The Stromboli volcano is well known for its periodic eruptions of small magnitude containing various proportions of ash, lava and gases. Imaging was carried out at a relatively high spectral and spatial resolution before and during eruptions from the North-East (NE) craters. Both sulfur dioxide (SO2) and sulfur tetrafluoride (SiF4) could be successfully identified within the volcano's plume from their distinct spectral features. During the passive degassing phase, a total amount of 3.3 kg of SO2 and 0.8 g of SiF4 were estimated. A violent eruption from NE1 crater was then observed and a total of 45 g and and 7 g of SO2 and SiF4 were estimated respectively. These results are in good agreement with previous work using a UV-SO2 camera. Finally, a smaller eruption from NE2 crater was observed. Total amounts of 3 kg and 17 g of SO2 and SiF4 were estimated respectively. Quantitative chemical maps for both gases will be presented. The results show that standoff thermal infrared hyperspectral imaging provides unique insights for a better understanding of volcanic eruptions.
Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Stern, Adrian
The acquisition of hyperspectral (HS) image datacubes with available 2D sensor arrays involves a time consuming scanning process. In the last decade, several compressive sensing (CS) techniques were proposed to reduce the HS acquisition time. In this paper, we present a method for near-infrared (NIR) HS imaging which relies on our rapid CS resonator spectroscopy technique. Within the framework of CS, and by using a modified Fabry–Perot resonator, a sequence of spectrally modulated images is used to recover NIR HS datacubes. Owing to the innovative CS design, we demonstrate the ability to reconstruct NIR HS images with hundreds of spectral bands from an order of magnitude fewer measurements, i.e. with a compression ratio of about 10:1. This high compression ratio, together with the high optical throughput of the system, facilitates fast acquisition of large HS datacubes.
Demos,; Stavros, G [Livermore, CA
An optical hyperspectral/multimodal imaging method and apparatus is utilized to provide high signal sensitivity for implementation of various optical imaging approaches. Such a system utilizes long working distance microscope objectives so as to enable off-axis illumination of predetermined tissue thereby allowing for excitation at any optical wavelength, simplifies design, reduces required optical elements, significantly reduces spectral noise from the optical elements and allows for fast image acquisition enabling high quality imaging in-vivo. Such a technology provides a means of detecting disease at the single cell level such as cancer, precancer, ischemic, traumatic or other type of injury, infection, or other diseases or conditions causing alterations in cells and tissue micro structures.
Full Text Available The capabilities of unmanned airborne systems (UAS have become diverse with the recent development of lightweight remote sensing instruments. In this paper, we demonstrate our custom integration of the state-of-the-art technologies within an unmanned aerial platform capable of high-resolution and high-accuracy laser scanning, hyperspectral imaging, and photographic imaging. The technological solution comprises the latest development of a completely autonomous, unmanned helicopter by Aeroscout, the Scout B1-100 UAV helicopter. The helicopter is powered by a gasoline two-stroke engine and it allows for integrating 18 kg of a customized payload unit. The whole system is modular providing flexibility of payload options, which comprises the main advantage of the UAS. The UAS integrates two kinds of payloads which can be altered. Both payloads integrate a GPS/IMU with a dual GPS antenna configuration provided by OXTS for accurate navigation and position measurements during the data acquisition. The first payload comprises a VUX-1 laser scanner by RIEGL and a Sony A6000 E-Mount photo camera. The second payload for hyperspectral scanning integrates a push-broom imager AISA KESTREL 10 by SPECIM. The UAS was designed for research of various aspects of landscape dynamics (landslides, erosion, flooding, or phenology in high spectral and spatial resolution.
Tao, Feifei; Ngadi, Michael
Meat is an important food item in human diet. Its production and consumption has greatly increased in the last decades with the development of economies and improvement of peoples' living standards. However, most of the traditional methods for evaluation of meat quality are time-consuming, laborious, inconsistent and destructive to samples, which make them not appropriate for a fast-paced production and processing environment. Development of innovative and non-destructive optical sensing techniques to facilitate simple, fast, and accurate evaluation of quality are attracting increasing attention in the food industry. Hyperspectral imaging is one of the promising techniques. It integrates the combined merits of imaging and spectroscopic techniques. This paper provides a comprehensive review on recent advances in evaluation of the important quality attributes of meat including color, marbling, tenderness, pH, water holding capacity, and also chemical composition attributes such as moisture content, protein content and fat content in pork, beef and lamb. In addition, the future potential applications and trends of hyperspectral imaging are also discussed in this paper.
García-Allende, P. Beatriz; Conde, Olga M.; Mirapeix, Jesus; Cobo, Adolfo; Lopez-Higuera, Jose M.
Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard.
Alexander W. Koch
Full Text Available This paper presents a low-cost hyperspectral measurement setup in a new application based on fluorescence detection in the visible (Vis wavelength range. The aim of the setup is to take hyperspectral fluorescence images of viscous materials. Based on these images, fluorescent and non-fluorescent impurities in the viscous materials can be detected. For the illumination of the measurement object, a narrow-band high-power light-emitting diode (LED with a center wavelength of 370 nm was used. The low-cost acquisition unit for the imaging consists of a linear variable filter (LVF and a complementary metal oxide semiconductor (CMOS 2D sensor array. The translucent wavelength range of the LVF is from 400 nm to 700 nm. For the confirmation of the concept, static measurements of fluorescent viscous materials with a non-fluorescent impurity have been performed and analyzed. With the presented setup, measurement surfaces in the micrometer range can be provided. The measureable minimum particle size of the impurities is in the nanometer range. The recording rate for the measurements depends on the exposure time of the used CMOS 2D sensor array and has been found to be in the microsecond range.
Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J
The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.
Zhou, Jiankang; Ji, Yiqun; Chen, Yuheng; Chen, Xinhua; Shen, Weimin
The designed hyperspectral imaging system is composed of three main parts, that is, optical subsystem, electronic subsystem and capturing subsystem. And a three-dimensional "image cube" can be obtained through push-broom. The fore-optics is commercial-off-the-shelf with high speed and three continuous zoom ratios. Since the dispersive imaging part is based on Offner relay configuration with an aberration-corrected convex grating, high power of light collection and variable view field are obtained. The holographic recording parameters of the convex grating are optimized, and the aberration of the Offner configuration dispersive system is balanced. The electronic system adopts module design, which can minimize size, mass, and power consumption. Frame transfer area-array CCD is chosen as the image sensor and the spectral line can be binned to achieve better SNR and sensitivity without any deterioration in spatial resolution. The capturing system based on the computer can set the capturing parameters, calibrate the spectrometer, process and display spectral imaging data. Laboratory calibrations are prerequisite for using precise spectral data. The spatial and spectral calibration minimize smile and keystone distortion caused by optical system, assembly and so on and fix positions of spatial and spectral line on the frame area-array CCD. Gases excitation lamp is used in smile calibration and the keystone calculation is carried out by different viewing field point source created by a series of narrow slit. The laboratory and field imaging results show that this pushbroom hyperspectral imaging system can acquire high quality spectral images.
Tiwari, K. C.; Arora, M. K.; Singh, D.
Hyperspectral data acquired over hundreds of narrow contiguous wavelength bands are extremely suitable for target detection due to their high spectral resolution. Though spectral response of every material is expected to be unique, but in practice, it exhibits variations, which is known as spectral variability. Most target detection algorithms depend on spectral modelling using a priori available target spectra In practice, target spectra is, however, seldom available a priori. Independent component analysis (ICA) is a new evolving technique that aims at finding out components which are statistically independent or as independent as possible. The technique therefore has the potential of being used for target detection applications. A assessment of target detection from hyperspectral images using ICA and other algorithms based on spectral modelling may be of immense interest, since ICA does not require a priori target information. The aim of this paper is, thus, to assess the potential of ICA based algorithm vis a vis other prevailing algorithms for military target detection. Four spectral matching algorithms namely Orthogonal Subspace Projection (OSP), Constrained Energy Minimisation (CEM), Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM), and four anomaly detection algorithms namely OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), Uniform Target Detector (UTD) and a combination of Reed-Xiaoli anomaly detector and Uniform Target Detector (RXD-UTD) were considered. The experiments were conducted using a set of synthetic and AVIRIS hyperspectral images containing aircrafts as military targets. A comparison of true positive and false positive rates of target detections obtained from ICA and other algorithms plotted on a receiver operating curves (ROC) space indicates the superior performance of the ICA over other algorithms.
Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.
Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.
Li, Jingwei; Cai, Fuhong; Dong, Yongjiang; Zhu, Zhenfeng; Sun, Xianhe; Zhang, Hequn; He, Sailing
In this study, a portable confocal hyperspectral microscope is developed. In traditional confocal laser scanning microscopes, scan lens and tube lens are utilized to achieve a conjugate relationship between the galvanometer and the back focal plane of the objective, in order to achieve a better resolution. However, these lenses make it difficult to scale down the volume of the system. In our portable confocal hyperspectral microscope (PCHM), the objective is placed directly next to the galvomirror. Thus, scan lens and tube lens are not included in our system and the size of this system is greatly reduced. Furthermore, the resolution is also acceptable in many biomedical and food-safety applications. Through reducing the optical length of the system, the signal detection efficiency is enhanced. This is conducive to realizing both the fluorescence and Raman hyperspectral imaging. With a multimode fiber as a pinhole, an improved image contrast is also achieved. Fluorescent spectral images for HeLa cells/fingers and Raman spectral images of kumquat pericarp are present. The spectral resolution and spatial resolutions are about 0.4 nm and 2.19 μm, respectively. These results demonstrate that this portable hyperspectral microscope can be used in in-vivo fluorescence imaging and in situ Raman spectral imaging.
Kampe, Thomas U.; Good, William S.
NASA's Sustainable Land Imaging (SLI) program, managed through the Earth Science Technology Office, aims to develop technologies that will provide future Landsat-like measurements. SLI aims to develop a new generation of smaller, more capable, less costly payloads that meet or exceed current imaging capabilities. One projects funded by this program is Ball's Compact Hyperspectral Prism Spectrometer (CHPS), a visible-to-shortwave imaging spectrometer that provides legacy Landsat data products as well as hyperspectral coverage suitable for a broad range of land science products. CHPS exhibits extremely low straylight and accommodates full aperture, full optical path calibration needed to ensure the high radiometric accuracy demanded by SLI measurement objectives. Low polarization sensitivity in visible to near-infrared bands facilitates coastal water science as first demonstrated by the exceptional performance of the Operational Land Imager. Our goal is to mature CHPS imaging spectrometer technology for infusion into the SLI program. Our effort builds on technology development initiated by Ball IRAD investment and includes laboratory and airborne demonstration, data distribution to science collaborators, and maturation of technology for spaceborne demonstration. CHPS is a three year program with expected exiting technology readiness of TRL-6. The 2013 NRC report Landsat and Beyond: Sustaining and Enhancing the Nations Land Imaging Program recommended that the nation should "maintain a sustained, space-based, land-imaging program, while ensuring the continuity of 42-years of multispectral information." We are confident that CHPS provides a path to achieve this goal while enabling new science measurements and significantly reducing the cost, size, and volume of the VSWIR instrument.
Wang, Jim Jing-Yan
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.
Wang, Jim Jing-Yan; Alzahrani, Majed A.; Gao, Xin
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation - maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.
Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso
Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.
Jun, Won; Kim, Moon S.; Chao, Kaunglin; Lefcourt, Alan M.; Roberts, Michael S.; McNaughton, James L.
We used a portable hyperspectral fluorescence imaging system to evaluate biofilm formations on four types of food processing surface materials including stainless steel, polypropylene used for cutting boards, and household counter top materials such as formica and granite. The objective of this investigation was to determine a minimal number of spectral bands suitable to differentiate microbial biofilm formation from the four background materials typically used during food processing. Ultimately, the resultant spectral information will be used in development of handheld portable imaging devices that can be used as visual aid tools for sanitation and safety inspection (microbial contamination) of the food processing surfaces. Pathogenic E. coli O157:H7 and Salmonella cells were grown in low strength M9 minimal medium on various surfaces at 22 +/- 2 °C for 2 days for biofilm formation. Biofilm autofluorescence under UV excitation (320 to 400 nm) obtained by hyperspectral fluorescence imaging system showed broad emissions in the blue-green regions of the spectrum with emission maxima at approximately 480 nm for both E. coli O157:H7 and Salmonella biofilms. Fluorescence images at 480 nm revealed that for background materials with near-uniform fluorescence responses such as stainless steel and formica cutting board, regardless of the background intensity, biofilm formation can be distinguished. This suggested that a broad spectral band in the blue-green regions can be used for handheld imaging devices for sanitation inspection of stainless, cutting board, and formica surfaces. The non-uniform fluorescence responses of granite make distinctions between biofilm and background difficult. To further investigate potential detection of the biofilm formations on granite surfaces with multispectral approaches, principal component analysis (PCA) was performed using the hyperspectral fluorescence image data. The resultant PCA score images revealed distinct contrast between
Full Text Available Faecal contamination of fresh fruits represents a severe danger for human health. Thus some techniques based on microbiological testing were developed to individuate faecal contaminants but those tests do not results efficient because their non-applicability on overall vegetable unity. In this work a methodology based on hyper-spectral fluorescence imaging was developed and tested to detecting faecal contamination on fresh tomatoes. Two image-processing methods were performed to maximise the contrast between the faecal contaminant and tomatoes skin: principal component analysis and band image ratio (BRI. The BRI method allows classifying correctly 70% of contaminated area, with no false-positives in all examined cases. Thus, the developed methodology can be employed for a fast and effective detection of faecal contamination on fresh tomatoes.
Thompson, David R.
Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.
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.
Soto, Juan; Granda, Guillermo; Prieto, Flavio; Ipanaque, William; Machacuay, Jorge
Nowadays, cocoa bean exportation from Piura-Peru is having a positive international market response due to their inherent high quality. Nevertheless, when using subjective techniques for quality assessment, such as the cut test, a wastefulness of grains is generated, additional to a restriction in the selection as well as improvement approaches in earlier stages for optimizing the quality. Thus, in an attempt to standardize the internal features analyzed by the cut test, for instance, crack formation and internal color changes during the fermentation, this research is submitted as an approach which aims to make use of hyperspectral images, with the purpose of having a quick and accurate analysis. Hyperspectral cube size was reduced by using Principal Component Analysis (PCA). The image generated by principal component PC1 provides enough information to clearly distinguish the internal cracks of the cocoa bean, since the zones where these cracks are, have a negative correlation with PC1. The features taken were processed through a fuzzy block, which is able to describe the cocoa bean quality. Three membership functions were defined in the output: unfermented, partly fermented and well fermented, by using trapezoidal-shaped and triangular-shaped functions. A total of twelve rules were propounded. Furthermore, the bisector method was chosen for the defuzzification. Begin the abstract two lines below author names and addresses.
Wu, Jiaji; Xu, Jianglei
The clustered DPCM (C-DPCM) lossless compression method by Jarno et al. for hyperspectral images achieved a good compression effect. It can be divided into three components: clustering, prediction, and coding. In the prediction part, it solves a multiple linear regression model for each of the clusters in every band. Without considering the effect of noise spectra, there is still room for improvement. This paper proposes a C-DPCM method with Removing Noise Spectra (C-DPCM-RNS) for the lossless compression of hyperspectral images. C-DPCM-RNS's prediction part consists of two-times trainings. The prediction coefficients obtained from the first training will be used in the linear predictor to compute all the predicted values and then the difference between original and predicted values in current band of current class. Only the non-noise spectra are used in the second training. The resulting prediction coefficients from the second training will be used for prediction and sent to the decoder. The two-times trainings remove part of the interference of noise spectra, and reaches a better compression effect than other methods based on regression prediction.
Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.
A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.
Full Text Available Classical chromatographic methods, such as ultra performance liquid chromatography (UPLC, are used as reference methods to assess seed quality and homogeneous pesticide coating of seeds. These methods have some important drawbacks since they are time consuming, expensive, destructive and require a substantial amount of solvent, among others. Near infrared (NIR spectroscopy seems to be an interesting alternative technique for the determination of the quality of seed treatment and avoids most of these drawbacks. The objective of this study was to assess the quality of pesticide coating treatment by near infrared hyperspectral imaging (NIR-HSI by analysing, on a seed-by-seed basis, several seeds simultaneously in comparison to NIR spectroscopy and UPLC as the reference method. To achieve this goal, discrimination—partial least squares discriminant analysis (PLS-DA—models and regression—partial least squares (PLS—models were developed. The results obtained by NIR-HSI are compared to the results obtained with NIR spectroscopy and UPLC instruments. This study has shown the potential of NIR hyperspectral imaging to assess the quality/homogeneity of the pesticide coating on seeds.
showing the signicant portion of image and video data transfers via Youtube , Facebook, and Flickr as primary platforms from Infographic (2015) digital...reserves • hydrography: lakes, rivers, streams, swamps, coastal flats • relief: mountains, valleys, slopes, depressions • vegetation: wooded and cleared
Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert
In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...
Howle, Chris R.; Spear, Abigail M.; Gazi, Ehsan; Crane, Nicole J.
In recent conflicts, battlefield injuries consist largely of extensive soft injuries from blasts and high energy projectiles, including gunshot wounds. Repair of these large, traumatic wounds requires aggressive surgical treatment, including multiple surgical debridements to remove devitalised tissue and to reduce bacterial load. Identifying those patients with wound complications, such as infection and impaired healing, could greatly assist health care teams in providing the most appropriate and personalised care for combat casualties. Candidate technologies to enable this benefit include the fusion of imaging and optical spectroscopy to enable rapid identification of key markers. Hence, a novel system based on IR negative contrast imaging (NCI) is presented that employs an optical parametric oscillator (OPO) source comprising a periodically-poled LiNbO3 (PPLN) crystal. The crystal operates in the shortwave and midwave IR spectral regions (ca. 1.5 - 1.9 μm and 2.4 - 3.8 μm, respectively). Wavelength tuning is achieved by translating the crystal within the pump beam. System size and complexity are minimised by the use of single element detectors and the intracavity OPO design. Images are composed by raster scanning the monochromatic beam over the scene of interest; the reflection and/or absorption of the incident radiation by target materials and their surrounding environment provide a method for spatial location. Initial results using the NCI system to characterise wound biopsies are presented here.
Full Text Available This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.
SEGMENTATION AND CLASSIFICATION OF BURN COLOR IMAGES Begoña Acha1, Carmen Serrano1, Laura Roa2 1Área de Teoría de la Señal y Comunicaciones ...2000, Las Vegas (USA), pp. 411-415.  G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New
.... In the classification part, we take advantage of color information by clustering, with a vector quantization algorithm, the color centroids of small squares, taken from the burnt segmented part of the image, in the (V1, V2) plane into two possible groups, where V1 and V2 are the two chrominance components of the CIE Lab representation.
Full Text Available , an automated method can be used to help identify human settlements in a fixed, repeatable and timely manner. The main contribution of this work is to improve generalisation on settlement type classification of aerial imagery. Images acquired at different dates...
Arngren, Morten; Hansen, Per Waaben; Eriksen, Birger
imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel’s lack of germination and is unable to identify dormancy, kernel damage etc. The analysis...... is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised...
Dewandel, Jean-Luc; Beghuin, Didier; Dubois, Xavier; Antoine, Philippe
Several applications require the identification of chemical elements during re-entry of material in the atmosphere. The materials can be from human origin or meteorites. The Automated Transfer Vehicle (ATV) re-entry has been filmed with conventional camera from airborne manual operation. In order to permit the identification of the separate elements from their glow, spectral analysis needs to be added to the video data. In a LET-SME contract with ESA, Lambda-X has built a Fourier Transform Imaging Spectrometer to permit, in a future work, to bring the technology to the readiness level required for the application. In this paper, the principles of the Fourier Transform Imaging spectroscopy are recalled, the different interferometers suitable for supporting the technique are reviewed and the selection process is explained. The final selection of the interferometer corresponds to a birefringent prism based common path shear interferometer. The design of the breadboard and its performances are presented in terms of spatial resolution, aperture, and spectral resolution. A discussion is open regarding perspective of the technique for other remote sensing applications compared to more usual push broom configurations.
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.
Honniball, C. I.; Wright, R.; Lucey, P. G.
The Mid-Wave InfraRed (MWIR) from 3 to 5 microns carries a wealth of information for both earth and planetary science applications. Molecules like methane and carbon dioxide exhibit prominent spectral features in the MWIR allowing us to detect their presences in the atmosphere after being released from volcanic vents, industrial gas leaks or biomass burning events. Energy released by wildfires at 4 μm is an important measurement for quantifying fire radiative power (FRP); an important climate variable that allows estimates of the amount of carbon liberated into the Earth's atmosphere during a burning event. FRP can also be used to estimate lava flow cooling rates and forecasting lava flow hazards. This spectral region also allows the derivation of temperatures from hot spots like the ones on Jupiter's moon Io, which provide important insights into the formation and evolution of Io. In the MWIR region there is limited signal available to measure for low temperature targets. This presents technical challenges on achieving high signal-to-noise ratios (SNR); therefore, acquiring adequate data in the MWIR is difficult without cryogenically cooling the instrument. Recent improvements to microbolometer technology and emerging interferometric techniques have allowed us to acquire good thermal infrared (TIR) data without the need for cooling. By coupling an uncooled microbolometer with a Sagnac interferometer we have demonstrated in the TIR that high SNR's can be obtained for hyperspectral imaging. To explore if this imaging technique holds in the MWIR, with funding from NASA, we have built, tested and compared two MWIR hyperspectral instruments, an uncooled microbolometer version and a liquid nitrogen cooled photon detector version with the same optical design. We demonstrate that using the aforementioned imaging technique we can achieve good SNR's for hyperspectral MWIR imaging using an uncooled instrument for targets 20°C above ambient. In late July 2017, we field
Full Text Available The German Aerospace Center DLR – namely the Earth Observation Center EOC and the German Space Operations Center GSOC – is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program. The Earth Observation Center has long lasting experiences with air- and spaceborne acquisition, processing, and analysis of hyperspectral image data. In the first part of this paper, an overview of the radiometric in-flight calibration concept including dark value measurements, deep space measurements, internal lamps measurements and sun measurements is presented. Complemented by pre-launch calibration and characterization these analyses will deliver a detailed and quantitative assessment of possible changes of spectral and radiometric characteristics of the hyperspectral instrument, e.g. due to degradation of single elements. A geometric accuracy of 100 m, which will be improved to 30 m with respect to a used reference image, if it exists, will be achieved by ground processing. Therfore, and for the required co-registration accuracy between SWIR and VNIR channels, additional to the radiometric calibration, also a geometric calibration is necessary. In the second part of this paper, the concept of the geometric calibration is presented in detail. The geometric processing of EnMAP scenes will be based on laboratory calibration results. During repeated passes over selected calibration areas images will be acquired. The update of geometric camera model parameters will be done by an adjustment using ground control points, which will be extracted by automatic image matching. In the adjustment, the improvements of the attitude angles (boresight angles, the improvements of the interior orientation (view vector and the improvements of the position data are estimated. In this paper, the improvement of the boresight angles is presented in detail as an example. The other
Schneider, M.; Müller, R.; Krawzcyk, H.; Bachmann, M.; Storch, T.; Mogulsky, V.; Hofer, S.
The German Aerospace Center DLR - namely the Earth Observation Center EOC and the German Space Operations Center GSOC - is responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Earth Observation Center has long lasting experiences with air- and spaceborne acquisition, processing, and analysis of hyperspectral image data. In the first part of this paper, an overview of the radiometric in-flight calibration concept including dark value measurements, deep space measurements, internal lamps measurements and sun measurements is presented. Complemented by pre-launch calibration and characterization these analyses will deliver a detailed and quantitative assessment of possible changes of spectral and radiometric characteristics of the hyperspectral instrument, e.g. due to degradation of single elements. A geometric accuracy of 100 m, which will be improved to 30 m with respect to a used reference image, if it exists, will be achieved by ground processing. Therfore, and for the required co-registration accuracy between SWIR and VNIR channels, additional to the radiometric calibration, also a geometric calibration is necessary. In the second part of this paper, the concept of the geometric calibration is presented in detail. The geometric processing of EnMAP scenes will be based on laboratory calibration results. During repeated passes over selected calibration areas images will be acquired. The update of geometric camera model parameters will be done by an adjustment using ground control points, which will be extracted by automatic image matching. In the adjustment, the improvements of the attitude angles (boresight angles), the improvements of the interior orientation (view vector) and the improvements of the position data are estimated. In this paper, the improvement of the boresight angles is presented in detail as an example. The other values and combinations
Windham, William R; Yoon, Seung-Chul; Ladely, Scott R; Haley, Jennifer A; Heitschmidt, Jerry W; Lawrence, Kurt C; Park, Bosoon; Narrang, Neelam; Cray, William C
The U.S. Department of Agriculture, Food Safety Inspection Service has determined that six non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) are adulterants in raw beef. Isolate and phenotypic discrimination of non-O157 STEC is problematic due to the lack of suitable agar media. The lack of distinct phenotypic color variation among non-O157serogroups cultured on chromogenic agar poses a challenge in selecting colonies for confirmation. In this study, visible and near-infrared hyperspectral imaging and chemometrics were used to detect and classify non-O157 STEC serogroups grown on Rainbow agar O157. The method was first developed by building spectral libraries for each serogroup obtained from ground-truth regions of interest representing the true identity of each pixel and thus each pure culture colony in the hyperspectral agar-plate image. The spectral library for the pure-culture non-O157 STEC consisted of 2,171 colonies, with spectra derived from 124,347 of pixels. The classification models for each serogroup were developed with a k nearest-neighbor classifier. The overall classification training accuracy at the colony level was 99%. The classifier was validated with ground beef enrichments artificially inoculated with 10, 50, and 100 CFU/ml STEC. The validation ground-truth regions of interest of the STEC target colonies consisted of 606 colonies, with 3,030 pixels of spectra. The overall classification accuracy was 98%. The average specificity of the method was 98% due to the low false-positive rate of 1.2%. The sensitivity ranged from 78 to 100% due to the false-negative rates of 22, 7, and 8% for O145, O45, and O26, respectively. This study showed the potential of visible and near-infrared hyperspectral imaging for detecting and classifying colonies of the six non-O157 STEC serogroups. The technique needs to be validated with bacterial cultures directly extracted from meat products and positive
Sanchez, Jose Enrique; Auge, Estanislau; Santalo, Josep; Blanes, Ian; Serra-Sagrista, Joan; Kiely, Aaron
A new standard for image coding is being developed by the MHDC working group of the CCSDS, targeting onboard compression of multi- and hyper-spectral imagery captured by aircraft and satellites. The proposed standard is based on the "Fast Lossless" adaptive linear predictive compressor, and is adapted to better overcome issues of onboard scenarios. In this paper, we present a review of the state of the art in this field, and provide an experimental comparison of the coding performance of the emerging standard in relation to other state-of-the-art coding techniques. Our own independent implementation of the MHDC Recommended Standard, as well as of some of the other techniques, has been used to provide extensive results over the vast corpus of test images from the CCSDS-MHDC.
Vasefi, Fartash; MacKinnon, Nicholas; Saager, Rolf B.; Durkin, Anthony J.; Chave, Robert; Lindsley, Erik H.; Farkas, Daniel L.
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.
Giannoni, Luca; Lange, Frédéric; Tachtsidis, Ilias
Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO2) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO2) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes.
Palsson, Frosti; Sveinsson, Johannes R.; Benediktsson, Jon Atli
The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused...... multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral...... information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four...
Bonifazi, Giuseppe; Serranti, Silvia; Trella, Agata; Garcia Izquierdo, Carlos
This work was carried out in the framework of the LIFE RESAFE Project (LIFE12 ENV/IT/000356) "Innovative fertilizer from urban waste, bio-char and farm residues as substitute of chemical fertilizers". The aim of RESAFE project is the production of a new fertilizer from waste for agricultural practices. The new fertilizer was tested on 5 different crops during field trials carried out in Spain: barley, corn, tomato, potato and melon. For each crop six different treatments were applied and compared to verify the quality of RESAFE fertilizer. Soil samples were collected at the beginning and at the end of the experiment. The possibility to apply hyperspectral imaging (HSI) to perform soil evolution monitoring and characterization in respect to the fertilizer utilization and quality of the resulting crops was investigated. Soil samples were acquired by HSI in the near infrared field (1000-1700 nm) and on the same samples classical chemical analyses were carried out with reference to total nitrogen, total organic carbon, C/N ratio, total organic matter. Hyperspectral data were analyzed adopting a chemometric approach through application of Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares Analysis (PLS) for estimation of chemical parameters. The results showed as the proposed hardware and software integrated architecture allows to implement low cost and easy to use analytical procedures able to quantitatively assess soil chemical-physical attributes according to different fertilization strategies, in respect of different environmental conditions and selected crops.
Meacham, K.; Montes, C.; Pederson, T.; Wu, J.; Guan, K.; Bernacchi, C.
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.
Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge
This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.
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