Hence , a discontinuity preserving relaxation ( DPR ) method can be used to locally smooth the results without crossing the boundaries by detecting the discontinuities of an image in advance . In this letter , we developed a novel strategy that combines these two methods to improve the hyperspectral image classification . A JSM is first applied to obtain a posteriori probability distribution of pixels and then a DPR method is used to further improve the classification results . Deep learning has achieved great successes in conventional computer vision tasks . In this paper , we exploit deep learning techniques to address the hyperspectral image classification problem . In contrast to conventional computer vision tasks that only examine the spatial context , our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification . Many studies [1]–[2 ] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem . Moreover , kernel-based methods have attracted much attention in the area of pattern recognition and machine learning , many researches [3]–[5 ] show that kernel method is computationally efficient , robust , and stable for pattern analysis . Obtaining labeled training sets for hyperspectral image data classification is often time consuming and expensive . Therefore , classification of hyperspectral data with insufficient training samples catches the attention of researchers lately . In the hyperspectral image classification literature , mean and covariance are prominently used statistics . The first order statistics , i.e. , mean , although easily estimated , can have low discriminative property . Subsequently , a composite kernel framework is utilized to balance the extracted spatial features and the original spectral features in the classifier . Our proposed method is shown to be effective for hyperspectral image classification purposes . Specifically , our experimental results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer and the reflective optics spectrographic imaging system demonstrate the effectiveness of the proposed method as compared to other state-of-the-art spatial-spectral classifiers . While the HAC algorithm is applied to EO-1 satellite Hyperion hyperspectral image and obtains the results of classification . Comparing with the hyperspectral image classifying results of K-MEANS , ISODATA and HAC classifiers , the HAC , as a unsupervised classification method , is confirmed to have better application on hyperspectral image classification . document : Shorten Spatial - spectral RNN with Parallel - GRU for Hyperspectral Image Classification Convolutional neural networks ( CNNs ) attained a good performance in hyperspectral sensing image ( HSI ) classification , but CNNs consider spectra as orderless vectors . Abstract . The independent component analysis has been commonly employed in hyperspectral unmixing . However , the success of this method is highly dependent on the independency of its sources assumption . Hyperspectral unmixing enables variety of applications like anomaly detection , change detection , mineral exploitation and manmade material identification and detection , and target detection . Hyperspectral unmixing involves two steps , first estimates the spectral signature of pure material presents in image , known as endmembers and second determines their proportion in mixed pixels , known as abundances . Vertex Component Algorithm ( VCA ) is a fast and powerful algorithm . The TV regularization term , however , usually brings some staircase effects . To alleviate this drawback , we propose a bilateral filter based TV regularization for hyperspectral image unmixing . Then we present an unmixing model that combines a data-fidelity term , a sparsity regularization term , and the new regularization term . It is natural to consider a sparse unmixing problem in a homogeneous region as a task . Experiments on signal reconstruction and hyperspectral image unmixing demonstrate the effectiveness of the proposed multitasking framework for sparse reconstruction . This paper presents a method to solve hyperspectral unmixing problem based on the well-known linear mixing model . Hyperspectral unmixing is to decompose observed spectrum of a mixed pixel into its constituent spectra and a set of corresponding abundances . Given the nature of many algorithmic approaches in hyperspectral imaging , however , and due to the increasing availability of high-bandwidth optical networks , wide-area data parallel execution may well be a feasible acceleration approach . This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications : automatic morphological endmember extraction and orthogonal subspace projection . It presents experimental results obtained on a real-world multicluster system , and provides a feasibility analysis of the applied parallelization approaches . In hyperspectral unmixing applications , one typically assumes that a single spectrum exists for every endmember . In many scenarios , this is not the case , and one requires a set or a distribution of spectra to represent an endmember or class . Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process . The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e. , the derivation of the spectral signatures of the endmembers ( endmembers ' extraction ) and the estimation of the abundance fractions of the pixels . In hyperspectral imagery , endmember extraction ( EE ) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature , endmembers , from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data . EE process has some difficulties . Unmixing results are compared to traditional NMF method based on SAD and AAD measures . Results demonstrate that this method can be used efficiently for hyperspectral unmixing purposes . This paper proposes a novel Bayesian Lasso model with spatially related sparsity for hyperspectral linear unmixing . Based on the sparsity hypothesis and the spatial correlation between pixels , we introduce Lasso penalty and spatially constrained prior distributions to the original Beyesian model . The linear mixing model is widely assumed when unmixing hyperspectral images , but it can not account for endmembers spectral variability . Thus , several workarounds have arisen in the hyperspectral unmixing literature , such as the extended linear mixing model ( ELMM ) , which authorizes endmembers to vary pixelwise according to scaling factors , or local spectral unmixing ( LSU ) where the unmixing process is conducted locally within the image . In the latter case however , results are difficult to interpret at the whole image scale . This paper presents a kernel-based feature selection method for the classification of hyperspectral images . The proposed method aims at selecting a subset of the original features that are both 1 ) relevant ( discriminant ) for the considered classification problem , i.e. , preserve the functional relationship between input and output variables , and 2 ) invariant ( stable ) across different domains , i.e. , minimize the data-set shift between the source and the target domains . Classification of Hyperspectral remote sensing images is an important research direction . Hyperspectral remote sensing images have high dimension and nonlinear property . Please see the column from Prof. Adriano Camps , IEEE Geoscience and Remote Sensing Society ( GRSS ) president , for a summary of a very busy week in Valencia , Spain . We lead this issue with a comprehensive review of spatial-spectral methods of classifying hyperspectral images , covering over 25 approaches , spanning five general branches : mathematical morphology , Markov random fields , segmentation , sparse representation , and deep learning . Hyperspectral image classification remains a hot area in remote sensing , with the number of publications continuing to show a steady growth over the last two decades . Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data . As a supplement or an alternative to classification of hyperspectral image data linear and semi-parametric mixture models are considered in order to obtain estimates of abundance of each class or end-member in pixels with mixed membership . Full unmixing based on both ordinary least squares ( OLS ) and non-negative least squares ( NNLS ) , and the partial unmixing methods orthogonal subspace projection ( OSP ) , constrained energy minimization ( CEM ) and an eigenvalue formulation alternative are dealt with . In this paper , we show how different types of nondiagonal potentials can be used within the SE framework in a way that allows for the integration of spatial and spectral information in unsupervised manifold learning and recovery . The nondiagonal potentials encode spatial proximity , which when combined with the spectral proximity information in the original graph , yields a framework that is competitive with state-of-the-art spectral/spatial fusion approaches for clustering and subsequent classification of hyperspectral image data . Feature extraction and feature selection are usual ways to overcome these problems . In this study , an optimal classification system for classifying hyperspectral image data is proposed . It is made up of orthonormal coordinate axes of the feature space . The simultaneous use of spatial and spectral information for the classification-based analysis of hyperspectral images improves classification results and image segmentation quality . In this paper pixel spectra are individually classified by conventional support vector machines ( SVM ) . Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction . Compared to other band selection methods , IFBS can effectively select the most significant bands for the image classification of hyperspectral images . The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications , such as forest inventories or conservation and management of ecosystems , for which species classification techniques and estimation of the number of individuals are highly valuable inputs . In this paper , we propose a method for hyperspectral image segmentation , based on the binary partition tree ( BPT ) algorithm , and we apply it to two sites located in Hawaiian and Panamean tropical rainforests . Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT . In this paper , we propose a spatial – spectral feature fusion model with a predictive feature weighting mechanism and demonstrate its applications to the problems of hyperspectral image classification and segmentation . To address these problems , we learn a set of 1-D convolutional local spectral filters and 2-D spatial – spectral filters that feed features into a fusion module , in an end-to-end fashion . This paper explores the performance of Ensembles of Extreme Learning Machine classifiers for hyperspectral image classification and segmentation in a semisupervised and spatially regularized process . The approach assumes that we have available only a small training set of labeled samples , which we enrich with a set of guessed labelings on selected samples from the vast pool of unlabeled image pixels . In this paper , endmember extraction method has been modified . Simple Linear Iterative Clustering(SLIC ) algorithm is used to segment hyperspectral image pixels into superpixel sets , and conducted experimental comparison of the impact of different dimension reduction methods , different data formats and different algorithms ' parameters on hyperspectral image superpixel segmentation results . Furthermore , the impact of SLIC superpixel segmentation results on two typical endmember extraction algorithms are analyzed . This paper develops three new versions of engropy rate superpixel segmentation ( ERS ) for hyperspectral image superpixel segmentation by modifying the design of weights of ERS . The three new designed weights are based on Euclidean Distance ( ED ) , Spectral Angle Distance ( SAD ) and spectral Correlation Coefficient ( COR ) respectively . Thus , efficient decomposition methods are required to compress the data and make it further suitable for other processes such as classification and pattern recognition . We present a simple and efficient multilinear principal component analysis method for hyperspectral image decomposition and compare the performance between the proposed tensor ( MPCA ) and matrix ( PCA ) based decomposition methods . Simulation results shows that performance does not degrade when tensor based methods are used for decomposition . Spectral-spatial classification of remotely sensed hyperspectral images has been the subject of many studies in recent years . Current methods achieve excellent performance on benchmark hyperspectral image labeling tasks when a sufficient number of labeled pixels is available . However , in the presence of only very few labeled pixels , such classification becomes a challenging problem . Due to the high dimensionality , large number of classes , presence of noise and complex structure , this is not a trivial task . There exists a need for more precise and computationally efficient algorithms for hyperspectral image segmentation and classification . In this paper , we introduce a novel algorithm for segmentation of hyperspectral images and selecting valuable pixels for classifier training procedure . Gradient images are usually implemented by traditional watershed algorithm with no-signal segmentation , which can easily cause over-segmentation . To overcome the defects and apply watershed algorithm to the complex hyperspectral remote sensing image segmentation , it combines with morphological preprocessing method in the text . An image is smooth processed , its gradient image is reconstructed by open and close reconstruction technology of morphology at the same time . Hyperspectral images surpass other imaging techniques in the detection of objects , classification and the detection of the changes that occur in the scene . A recent approach for hyperspectral segmentation is the superpixel segmentation approach . In this work , simple linear iterative clustering based superpixel segmentation method is adapted to hyperspectral images , and the effect of various metrics such as Euclidian distance , spectral information divergence , spectral angular distance , and the principal component analysis and minimum noise fraction transforms on the superpixel segmentation performance are evaluated . Many deep learning models , such as convolutional neural network ( CNN ) and recurrent neural network ( RNN ) , have been successfully applied to extracting deep features for hyperspectral tasks . Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information . We report a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile for hyperspectral , fluorescence , or principal component imaging . Most hyperspectral or multispectral imaging systems use wavelength selection devices such as acousto-optic tunable filters ( AOTFs ) , tunable grating or prism-based monochromators , or filter wheels . The first major contribution of the thesis is to introduce Stein ’s unbiased risk estimator ( SURE ) as an automatic technique for model and tuning parameter selection ( including rank selection ) for hyperspectral image analysis . As a result , this thesis proposes two automatic SURE-based techniques for both hyperspectral rank selection and hyperspectral image restoration ( called SPAMARS and SPAWMARS ) . Proposing a new and fast restoration method ( called FORPDN ) based on classical linear model and inspired by the specific spectral characteristic of hyperspectral image is the next contribution of the thesis . In this paper , we propose a novel ensemble margin based semi-supervised random forest ( EMRF ) algorithm for the classification of the hyperspectral image with limited training data . The proposed method tries to improve the effectiveness of the ensemble model via adaptively labeling the unlabeled instances with high classification probability then adding them into the training set . This paper proposes a new semisupervised segmentation algorithm for hyperspectral image segmentation . The algorithm steps are as follows : 1 ) supervised training an initial classifier from a small balanced training set , 2 ) clustering of the image pixels , by a k-means algorithm 3 ) adding unlabeled pixels to the original training data set according to the spatial neighborhood and the cluster membership , 4 ) supervised training of the classifier with the enriched training data set , 6 ) classification of the hyperspectral image 4 ) spatial regularization of classification results consisting in selecting the most frequent class in each pixel neighborhood . In this work we use the Hybrid Extreme Rotation Forest ( HERF ) which has been successfully applied in medical image domain . Anomaly detection aims to separate anomalous pixels from the background , and has become an important application of remotely sensed hyperspectral image processing . Anomaly detection methods based on low-rank and sparse representation ( LRASR ) can accurately detect anomalous pixels . One of the most crucial issues to judge image quality is the spatial resolution . Hyperspectral image ( HSI ) sharpening is the process of combining spatial information to enhance spatial resolution of HSIs . Huge volumes of HSI data cause difficulties during the sharpening process . Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics , especially in the local feature representation phase . However , the study of acutance in this application has not received a lot of attention . Furthermore , our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral . From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics . Efficient , high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging . A novel method of matched filtering known as science based multivariate calibration ( SBC ) was developed for hyperspectral calibration . Classical ( MLR ) and inverse ( PLS , PCR ) methods are combined by spectroscopically measuring the spectral " signal " and by statistically estimating the spectral " noise . " Deep neural networks can learn deep feature representation for hyperspectral image ( HSI ) interpretation and achieve high classification accuracy in different datasets . However , counterintuitively , the classification performance of deep learning models degrades as their depth increases . In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework . The proposed approach is based on a Hidden Markov Modeling ( HMM ) of the images with common segmentation , or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field . The experimental result also indicates that the initial scale affects the fractal signature value , and the number of bands affects both fractal signature value and feature scale . The primary result implies that the multifractal based method may be a feasible approach to mining hyperspectral data . document : Shorten Spatial - spectral RNN with Parallel - GRU for Hyperspectral Image Classification Convolutional neural networks ( CNNs ) attained a good performance in hyperspectral sensing image ( HSI ) classification , but CNNs consider spectra as orderless vectors . Therefore , considering the spectra as sequences , recurrent neural networks ( RNNs ) have been applied in HSI classification , for RNNs is skilled at dealing with sequential data .
Hence , a discontinuity preserving relaxation ( DPR ) method can be used to locally smooth the results without crossing the boundaries by detecting the discontinuities of an image in advance . In this letter , we developed a novel strategy that combines these two methods to improve the hyperspectral image classification . A JSM is first applied to obtain a posteriori probability distribution of pixels and then a DPR method is used to further improve the classification results .
Deep learning has achieved great successes in conventional computer vision tasks . In this paper , we exploit deep learning techniques to address the hyperspectral image classification problem . In contrast to conventional computer vision tasks that only examine the spatial context , our proposed method can exploit both spatial context and spectral correlation to enhance hyperspectral image classification .
Many studies [1]–[2 ] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem . Moreover , kernel-based methods have attracted much attention in the area of pattern recognition and machine learning , many researches [3]–[5 ] show that kernel method is computationally efficient , robust , and stable for pattern analysis .
Obtaining labeled training sets for hyperspectral image data classification is often time consuming and expensive . Therefore , classification of hyperspectral data with insufficient training samples catches the attention of researchers lately .
In the hyperspectral image classification literature , mean and covariance are prominently used statistics . The first order statistics , i.e. , mean , although easily estimated , can have low discriminative property .
Subsequently , a composite kernel framework is utilized to balance the extracted spatial features and the original spectral features in the classifier . Our proposed method is shown to be effective for hyperspectral image classification purposes . Specifically , our experimental results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer and the reflective optics spectrographic imaging system demonstrate the effectiveness of the proposed method as compared to other state-of-the-art spatial-spectral classifiers .
While the HAC algorithm is applied to EO-1 satellite Hyperion hyperspectral image and obtains the results of classification . Comparing with the hyperspectral image classifying results of K-MEANS , ISODATA and HAC classifiers , the HAC , as a unsupervised classification method , is confirmed to have better application on hyperspectral image classification .
document : Shorten Spatial - spectral RNN with Parallel - GRU for Hyperspectral Image Classification Convolutional neural networks ( CNNs ) attained a good performance in hyperspectral sensing image ( HSI ) classification , but CNNs consider spectra as orderless vectors .
Abstract . The independent component analysis has been commonly employed in hyperspectral unmixing . However , the success of this method is highly dependent on the independency of its sources assumption .
Hyperspectral unmixing enables variety of applications like anomaly detection , change detection , mineral exploitation and manmade material identification and detection , and target detection . Hyperspectral unmixing involves two steps , first estimates the spectral signature of pure material presents in image , known as endmembers and second determines their proportion in mixed pixels , known as abundances . Vertex Component Algorithm ( VCA ) is a fast and powerful algorithm .
The TV regularization term , however , usually brings some staircase effects . To alleviate this drawback , we propose a bilateral filter based TV regularization for hyperspectral image unmixing . Then we present an unmixing model that combines a data-fidelity term , a sparsity regularization term , and the new regularization term .
It is natural to consider a sparse unmixing problem in a homogeneous region as a task . Experiments on signal reconstruction and hyperspectral image unmixing demonstrate the effectiveness of the proposed multitasking framework for sparse reconstruction .
This paper presents a method to solve hyperspectral unmixing problem based on the well-known linear mixing model . Hyperspectral unmixing is to decompose observed spectrum of a mixed pixel into its constituent spectra and a set of corresponding abundances .
Given the nature of many algorithmic approaches in hyperspectral imaging , however , and due to the increasing availability of high-bandwidth optical networks , wide-area data parallel execution may well be a feasible acceleration approach . This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications : automatic morphological endmember extraction and orthogonal subspace projection . It presents experimental results obtained on a real-world multicluster system , and provides a feasibility analysis of the applied parallelization approaches .
In hyperspectral unmixing applications , one typically assumes that a single spectrum exists for every endmember . In many scenarios , this is not the case , and one requires a set or a distribution of spectra to represent an endmember or class .
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process . The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e. , the derivation of the spectral signatures of the endmembers ( endmembers ' extraction ) and the estimation of the abundance fractions of the pixels .
In hyperspectral imagery , endmember extraction ( EE ) is a main stage in hyperspectral unmixing process where its role lies in extracting distinct spectral signature , endmembers , from hyperspectral image which is considered as the main input for unsupervised hyperspectral unmixing to generate the abundance fractions for every pixel in hyperspectral data . EE process has some difficulties .
Unmixing results are compared to traditional NMF method based on SAD and AAD measures . Results demonstrate that this method can be used efficiently for hyperspectral unmixing purposes .
This paper proposes a novel Bayesian Lasso model with spatially related sparsity for hyperspectral linear unmixing . Based on the sparsity hypothesis and the spatial correlation between pixels , we introduce Lasso penalty and spatially constrained prior distributions to the original Beyesian model .
The linear mixing model is widely assumed when unmixing hyperspectral images , but it can not account for endmembers spectral variability . Thus , several workarounds have arisen in the hyperspectral unmixing literature , such as the extended linear mixing model ( ELMM ) , which authorizes endmembers to vary pixelwise according to scaling factors , or local spectral unmixing ( LSU ) where the unmixing process is conducted locally within the image . In the latter case however , results are difficult to interpret at the whole image scale .
This paper presents a kernel-based feature selection method for the classification of hyperspectral images . The proposed method aims at selecting a subset of the original features that are both 1 ) relevant ( discriminant ) for the considered classification problem , i.e. , preserve the functional relationship between input and output variables , and 2 ) invariant ( stable ) across different domains , i.e. , minimize the data-set shift between the source and the target domains .
Classification of Hyperspectral remote sensing images is an important research direction . Hyperspectral remote sensing images have high dimension and nonlinear property .
Please see the column from Prof. Adriano Camps , IEEE Geoscience and Remote Sensing Society ( GRSS ) president , for a summary of a very busy week in Valencia , Spain . We lead this issue with a comprehensive review of spatial-spectral methods of classifying hyperspectral images , covering over 25 approaches , spanning five general branches : mathematical morphology , Markov random fields , segmentation , sparse representation , and deep learning . Hyperspectral image classification remains a hot area in remote sensing , with the number of publications continuing to show a steady growth over the last two decades .
Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data . As a supplement or an alternative to classification of hyperspectral image data linear and semi-parametric mixture models are considered in order to obtain estimates of abundance of each class or end-member in pixels with mixed membership . Full unmixing based on both ordinary least squares ( OLS ) and non-negative least squares ( NNLS ) , and the partial unmixing methods orthogonal subspace projection ( OSP ) , constrained energy minimization ( CEM ) and an eigenvalue formulation alternative are dealt with .
In this paper , we show how different types of nondiagonal potentials can be used within the SE framework in a way that allows for the integration of spatial and spectral information in unsupervised manifold learning and recovery . The nondiagonal potentials encode spatial proximity , which when combined with the spectral proximity information in the original graph , yields a framework that is competitive with state-of-the-art spectral/spatial fusion approaches for clustering and subsequent classification of hyperspectral image data .
Feature extraction and feature selection are usual ways to overcome these problems . In this study , an optimal classification system for classifying hyperspectral image data is proposed . It is made up of orthonormal coordinate axes of the feature space .
The simultaneous use of spatial and spectral information for the classification-based analysis of hyperspectral images improves classification results and image segmentation quality . In this paper pixel spectra are individually classified by conventional support vector machines ( SVM ) .
Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction . Compared to other band selection methods , IFBS can effectively select the most significant bands for the image classification of hyperspectral images .
The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications , such as forest inventories or conservation and management of ecosystems , for which species classification techniques and estimation of the number of individuals are highly valuable inputs . In this paper , we propose a method for hyperspectral image segmentation , based on the binary partition tree ( BPT ) algorithm , and we apply it to two sites located in Hawaiian and Panamean tropical rainforests . Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT .
In this paper , we propose a spatial – spectral feature fusion model with a predictive feature weighting mechanism and demonstrate its applications to the problems of hyperspectral image classification and segmentation . To address these problems , we learn a set of 1-D convolutional local spectral filters and 2-D spatial – spectral filters that feed features into a fusion module , in an end-to-end fashion .
This paper explores the performance of Ensembles of Extreme Learning Machine classifiers for hyperspectral image classification and segmentation in a semisupervised and spatially regularized process . The approach assumes that we have available only a small training set of labeled samples , which we enrich with a set of guessed labelings on selected samples from the vast pool of unlabeled image pixels .
In this paper , endmember extraction method has been modified . Simple Linear Iterative Clustering(SLIC ) algorithm is used to segment hyperspectral image pixels into superpixel sets , and conducted experimental comparison of the impact of different dimension reduction methods , different data formats and different algorithms ' parameters on hyperspectral image superpixel segmentation results . Furthermore , the impact of SLIC superpixel segmentation results on two typical endmember extraction algorithms are analyzed .
This paper develops three new versions of engropy rate superpixel segmentation ( ERS ) for hyperspectral image superpixel segmentation by modifying the design of weights of ERS . The three new designed weights are based on Euclidean Distance ( ED ) , Spectral Angle Distance ( SAD ) and spectral Correlation Coefficient ( COR ) respectively .
Thus , efficient decomposition methods are required to compress the data and make it further suitable for other processes such as classification and pattern recognition . We present a simple and efficient multilinear principal component analysis method for hyperspectral image decomposition and compare the performance between the proposed tensor ( MPCA ) and matrix ( PCA ) based decomposition methods . Simulation results shows that performance does not degrade when tensor based methods are used for decomposition .
Spectral-spatial classification of remotely sensed hyperspectral images has been the subject of many studies in recent years . Current methods achieve excellent performance on benchmark hyperspectral image labeling tasks when a sufficient number of labeled pixels is available . However , in the presence of only very few labeled pixels , such classification becomes a challenging problem .
Due to the high dimensionality , large number of classes , presence of noise and complex structure , this is not a trivial task . There exists a need for more precise and computationally efficient algorithms for hyperspectral image segmentation and classification . In this paper , we introduce a novel algorithm for segmentation of hyperspectral images and selecting valuable pixels for classifier training procedure .
Gradient images are usually implemented by traditional watershed algorithm with no-signal segmentation , which can easily cause over-segmentation . To overcome the defects and apply watershed algorithm to the complex hyperspectral remote sensing image segmentation , it combines with morphological preprocessing method in the text . An image is smooth processed , its gradient image is reconstructed by open and close reconstruction technology of morphology at the same time .
Hyperspectral images surpass other imaging techniques in the detection of objects , classification and the detection of the changes that occur in the scene . A recent approach for hyperspectral segmentation is the superpixel segmentation approach . In this work , simple linear iterative clustering based superpixel segmentation method is adapted to hyperspectral images , and the effect of various metrics such as Euclidian distance , spectral information divergence , spectral angular distance , and the principal component analysis and minimum noise fraction transforms on the superpixel segmentation performance are evaluated .
Many deep learning models , such as convolutional neural network ( CNN ) and recurrent neural network ( RNN ) , have been successfully applied to extracting deep features for hyperspectral tasks . Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information .
We report a spectrally and temporally programmable light engine based on a spatial light modulator that can dynamically create any narrow or broadband spectral profile for hyperspectral , fluorescence , or principal component imaging . Most hyperspectral or multispectral imaging systems use wavelength selection devices such as acousto-optic tunable filters ( AOTFs ) , tunable grating or prism-based monochromators , or filter wheels .
The first major contribution of the thesis is to introduce Stein ’s unbiased risk estimator ( SURE ) as an automatic technique for model and tuning parameter selection ( including rank selection ) for hyperspectral image analysis . As a result , this thesis proposes two automatic SURE-based techniques for both hyperspectral rank selection and hyperspectral image restoration ( called SPAMARS and SPAWMARS ) . Proposing a new and fast restoration method ( called FORPDN ) based on classical linear model and inspired by the specific spectral characteristic of hyperspectral image is the next contribution of the thesis .
In this paper , we propose a novel ensemble margin based semi-supervised random forest ( EMRF ) algorithm for the classification of the hyperspectral image with limited training data . The proposed method tries to improve the effectiveness of the ensemble model via adaptively labeling the unlabeled instances with high classification probability then adding them into the training set .
This paper proposes a new semisupervised segmentation algorithm for hyperspectral image segmentation . The algorithm steps are as follows : 1 ) supervised training an initial classifier from a small balanced training set , 2 ) clustering of the image pixels , by a k-means algorithm 3 ) adding unlabeled pixels to the original training data set according to the spatial neighborhood and the cluster membership , 4 ) supervised training of the classifier with the enriched training data set , 6 ) classification of the hyperspectral image 4 ) spatial regularization of classification results consisting in selecting the most frequent class in each pixel neighborhood . In this work we use the Hybrid Extreme Rotation Forest ( HERF ) which has been successfully applied in medical image domain .
Anomaly detection aims to separate anomalous pixels from the background , and has become an important application of remotely sensed hyperspectral image processing . Anomaly detection methods based on low-rank and sparse representation ( LRASR ) can accurately detect anomalous pixels .
One of the most crucial issues to judge image quality is the spatial resolution . Hyperspectral image ( HSI ) sharpening is the process of combining spatial information to enhance spatial resolution of HSIs . Huge volumes of HSI data cause difficulties during the sharpening process .
Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics , especially in the local feature representation phase . However , the study of acutance in this application has not received a lot of attention .
Furthermore , our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral . From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics .
Efficient , high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging . A novel method of matched filtering known as science based multivariate calibration ( SBC ) was developed for hyperspectral calibration . Classical ( MLR ) and inverse ( PLS , PCR ) methods are combined by spectroscopically measuring the spectral " signal " and by statistically estimating the spectral " noise . "
Deep neural networks can learn deep feature representation for hyperspectral image ( HSI ) interpretation and achieve high classification accuracy in different datasets . However , counterintuitively , the classification performance of deep learning models degrades as their depth increases .
In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework . The proposed approach is based on a Hidden Markov Modeling ( HMM ) of the images with common segmentation , or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field .
The experimental result also indicates that the initial scale affects the fractal signature value , and the number of bands affects both fractal signature value and feature scale . The primary result implies that the multifractal based method may be a feasible approach to mining hyperspectral data .
document : Shorten Spatial - spectral RNN with Parallel - GRU for Hyperspectral Image Classification Convolutional neural networks ( CNNs ) attained a good performance in hyperspectral sensing image ( HSI ) classification , but CNNs consider spectra as orderless vectors . Therefore , considering the spectra as sequences , recurrent neural networks ( RNNs ) have been applied in HSI classification , for RNNs is skilled at dealing with sequential data .
Hyperspectral Image Classification Using Joint Sparse Model and Discontinuity Preserving Relaxation Hyperspectral Image Classification With Deep Learning Models An automatic method to determine the coefficient of the composite kernel for hyperspectral image classification Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm Main Direction—An Effective Statistic for Hyperspectral Image Classification Spatial–Spectral Hyperspectral Image Classification Using Random Multiscale Representation [A New HAC Unsupervised Classifier Based on Spectral Harmonic Analysis]. Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification Application of Gibbs sampling in efficient hyperspectral unmixing based on the mixtures of Dirichlet components Anomaly detection using VCA algorithm for multi-temporal hyperspectral images Bilateral filter based total variation regularization for sparse hyperspectral image unmixing Evolutionary Multitasking Sparse Reconstruction: Framework and Case Study A projected gradient-based algorithm to unmix hyperspectral data Parallel hyperspectral image processing on distributed multicluster systems Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images New Divide and Conquer Method on Endmember Extraction Techniques Unmixing of hyperspectral data using robust statistics-based NMF A novel Bayesian lasso model based on spatial-correlated sparsity for semisupervised hyperspectral unmixing From local to global unmixing of hyperspectral images to reveal spectral variability Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning Band selection based gaussian processes for hyperspectral remote sensing images classification A Busy Week at IGARSS and a Full Issue of Features [From the Editor] Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data Schroedinger Eigenmaps with nondiagonal potentials for spatial-spectral clustering of hyperspectral imagery An Optimal Nonparametric Weighted System for Hyperspectral Data Classification Lattice Auto-Associative Memories Induced Multivariate Morphology for Hyperspectral Image Spectral-Spatial Classification Band selection for hyperspectral images based on impurity function On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images Feature Fusion With Predictive Weighting for Spectral Image Classification and Segmentation Spatially regularized semisupervised Ensembles of Extreme Learning Machines for hyperspectral image segmentation A hyperspectral image spectral unmixing method integrating slic superpixel segmentation Different Versions of Entropy Rate Superpixel Segmentation For Hyperspectral Image An efficient tensor based decomposition of hyperspectral image representation Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images Hyperspectral Image Analysis Based on Quad Tree Decomposition Segmentation of Hyperspectral Remote Sensing Image Based on Morphological Gradient Reconstruction and Marker Extraction Metric and transform performance analysis for hyperspectral superpixel segmentation Spectral-Spatial Attention Networks for Hyperspectral Image Classification Multispectral endoscopy and microscopy imaging system using a spectrally programmable light engine Sparse Hyperspectral Image Modeling and Restoration Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data On spatial regularization for semisupervised hyperspectral image segmentation using hybrid extreme rotation forest A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images Sub-block PCA-wavelet image sharpening approach for hyperspectral images Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance Novel hyperspectral prediction method and apparatus Deep residual networks for hyperspectral image classification Bayesian segmentation of hyperspectral images Multifractal based hyperion hyperspectral data mining Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
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IGARSS ICNC-FSKD ArXiv EUSIPCO IGARSS WHISPERS ICIP KES (1) CORES IGARSS WHISPERS SOCO-CISIS-ICEUTE SIU IGARSS WHISPERS IGARSS IGARSS FSKD ArXiv
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