Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071802
Yuan Zhou, Anand Rangarajan, P. Gader
Endmember variability complicates the problem of spectral unmixing. This variability is typically represented by probability distributions or spectral libraries. The present work describes a new distributional representation based on Gaussian Mixture Models (GMMs). The most common form in this setting is the Normal Compositional Model (NCM), wherein the endmembers for each pixel are modeled as samples drawn from unimodal Gaussians. In reality, however, the distribution of spectra from a material may be multi-modal. We first show that a linear combination of GMM random variables is also a GMM. This allows us to probabilistically formulate hyperspectral pixel likelihoods as combinations of independent endmember random variables. Then, after adding a reasonable smoothness and sparsity prior on the abundances, we obtain a standard Bayesian maximum a posteriori (MAP) problem for abundance and endmember parameter estimation. A generalized expectation-maximization (EM) algorithm is used to minimize the MAP objective function. We tested the GMM approach on two real datasets, and showcased its efficacy for modeling endmember variability by comparing it to current popular methods.
{"title":"A Gaussian mixture model representation of endmember variability for spectral unmixing","authors":"Yuan Zhou, Anand Rangarajan, P. Gader","doi":"10.1109/WHISPERS.2016.8071802","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071802","url":null,"abstract":"Endmember variability complicates the problem of spectral unmixing. This variability is typically represented by probability distributions or spectral libraries. The present work describes a new distributional representation based on Gaussian Mixture Models (GMMs). The most common form in this setting is the Normal Compositional Model (NCM), wherein the endmembers for each pixel are modeled as samples drawn from unimodal Gaussians. In reality, however, the distribution of spectra from a material may be multi-modal. We first show that a linear combination of GMM random variables is also a GMM. This allows us to probabilistically formulate hyperspectral pixel likelihoods as combinations of independent endmember random variables. Then, after adding a reasonable smoothness and sparsity prior on the abundances, we obtain a standard Bayesian maximum a posteriori (MAP) problem for abundance and endmember parameter estimation. A generalized expectation-maximization (EM) algorithm is used to minimize the MAP objective function. We tested the GMM approach on two real datasets, and showcased its efficacy for modeling endmember variability by comparing it to current popular methods.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133796609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071690
R. Roscher, J. Behmann, Anne-Katrin Mahlein, L. Plümer
We analyze the benefit of using topographic dictionaries for a sparse representation (SR) approach for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Topographic dictionaries are an arranged set of basis elements in which neighbored dictionary elements tend to cause similar activations in the SR approach. In this paper, the dictionary is obtained from samples of a healthy plant and partly build in a topographic way by using hyperspectral as well as geometry information, i.e. depth and inclination. It turns out that hyperspectral signals of leafs show a typical structure depending on depth and inclination and thus, both influences can be disentangled in our approach. Rare signals which do not fit into this model, e.g. leaf veins, are also captured in the dictionary in a non-topographic way. A reconstruction error index is used as indicator, in which disease symptoms can be distinguished from healthy plant regions. The advantage of the presented approach is that full spectral and geometry information is needed only once to built the dictionary, whereas the sparse reconstruction is done solely on hyperspectral information.
{"title":"On the benefit of topographic dictionaries for detecting disease symptoms on hyperspectral 3D plant models","authors":"R. Roscher, J. Behmann, Anne-Katrin Mahlein, L. Plümer","doi":"10.1109/WHISPERS.2016.8071690","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071690","url":null,"abstract":"We analyze the benefit of using topographic dictionaries for a sparse representation (SR) approach for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Topographic dictionaries are an arranged set of basis elements in which neighbored dictionary elements tend to cause similar activations in the SR approach. In this paper, the dictionary is obtained from samples of a healthy plant and partly build in a topographic way by using hyperspectral as well as geometry information, i.e. depth and inclination. It turns out that hyperspectral signals of leafs show a typical structure depending on depth and inclination and thus, both influences can be disentangled in our approach. Rare signals which do not fit into this model, e.g. leaf veins, are also captured in the dictionary in a non-topographic way. A reconstruction error index is used as indicator, in which disease symptoms can be distinguished from healthy plant regions. The advantage of the presented approach is that full spectral and geometry information is needed only once to built the dictionary, whereas the sparse reconstruction is done solely on hyperspectral information.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115688389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071708
A. Koz, Akin Caliskan, Aydin Alatan
Previously proposed hyperspectral image registration methods mostly focus on the registration of the images including overlapping bands in VNIR and SWIR range. In contrary to previous methods, we investigate the registration of hyperspectral images with no-overlapping bands in MWIR and LWIR range in this paper. The proposed approach achieves the image registration over 2D maps extracted from 3D hyperspectral data cubes. Considering that the main component of the captured signal in MWIR-LWIR range is thermal radiation, we first propose to use the brightness-temperature estimate of hyperspectral pixels to form the 2D image. In addition, hyperspectral pixel energy, average emissivity and the first three components of principal component analysis (PCA) transform are also utilized and tested for 3D-2D conversion. The performance of the methods are evaluated by the matching ratio of the interest points and by generating mosaic images from the given maps. The experimental results indicate that brightness-temperature estimate, pixel energy and first principal component gives comparable results for image matching. The emissivity maps and the remaining principal components are found to be not successful for image registration as these features do not form a common base for different band signals.
{"title":"Registration of MWIR-LWIR band hyperspectral images","authors":"A. Koz, Akin Caliskan, Aydin Alatan","doi":"10.1109/WHISPERS.2016.8071708","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071708","url":null,"abstract":"Previously proposed hyperspectral image registration methods mostly focus on the registration of the images including overlapping bands in VNIR and SWIR range. In contrary to previous methods, we investigate the registration of hyperspectral images with no-overlapping bands in MWIR and LWIR range in this paper. The proposed approach achieves the image registration over 2D maps extracted from 3D hyperspectral data cubes. Considering that the main component of the captured signal in MWIR-LWIR range is thermal radiation, we first propose to use the brightness-temperature estimate of hyperspectral pixels to form the 2D image. In addition, hyperspectral pixel energy, average emissivity and the first three components of principal component analysis (PCA) transform are also utilized and tested for 3D-2D conversion. The performance of the methods are evaluated by the matching ratio of the interest points and by generating mosaic images from the given maps. The experimental results indicate that brightness-temperature estimate, pixel energy and first principal component gives comparable results for image matching. The emissivity maps and the remaining principal components are found to be not successful for image registration as these features do not form a common base for different band signals.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071800
Utsav B. Gewali, S. Monteiro
Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex nonlinear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.
{"title":"Multitask learning of vegetation biochemistry from hyperspectral data","authors":"Utsav B. Gewali, S. Monteiro","doi":"10.1109/WHISPERS.2016.8071800","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071800","url":null,"abstract":"Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex nonlinear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123532779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071795
A. Marinoni, H. Clenet
Typically, quantitative interpretation of Mars mineralogy from spectra can be retrieved by analyzing the overlaps of absorption features. It is possible to achieve a thorough description of the abundances of each mineral the considered scene is composed of by applying proper deconvolution techniques such as those based on modified Gaussian model (MGM). However, MGM-based methods are sensitive on initial parameters for statistical distribution definition, or they are very time consuming when fully automatized. In this paper, a new method for identification of minerals on Mars surface by means of higher order nonlinear hyperspectral unmixing framework is introduced. Abundance distribution of magmatic minerals (olivine and pyroxenes) compounds is retrieved according to polytope decomposition algorithm. Experimental results show how the proposed method is able to provide actual abundance maps which are highly correlated to those obtained by an automatized MGM-based technique.
{"title":"Identification of mafic minerals on Mars by nonlinear hyperspectral unmixing","authors":"A. Marinoni, H. Clenet","doi":"10.1109/WHISPERS.2016.8071795","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071795","url":null,"abstract":"Typically, quantitative interpretation of Mars mineralogy from spectra can be retrieved by analyzing the overlaps of absorption features. It is possible to achieve a thorough description of the abundances of each mineral the considered scene is composed of by applying proper deconvolution techniques such as those based on modified Gaussian model (MGM). However, MGM-based methods are sensitive on initial parameters for statistical distribution definition, or they are very time consuming when fully automatized. In this paper, a new method for identification of minerals on Mars surface by means of higher order nonlinear hyperspectral unmixing framework is introduced. Abundance distribution of magmatic minerals (olivine and pyroxenes) compounds is retrieved according to polytope decomposition algorithm. Experimental results show how the proposed method is able to provide actual abundance maps which are highly correlated to those obtained by an automatized MGM-based technique.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122093781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071741
L. Xuan, Z. Ye, Junping Zhang
This paper presented a new approach called bi-inverted Gaussian model to calculated the diagnostic characteristic parameters of vegetation spectral. And used the parameters calculated from Hyperion image to make water content mapping. Using laboratory experiment measuring data, the relationships between absorption depth and the vegetation water content (VWC) were calculated. between absorption depth and VWC was 0.868 and the RMSE was 0.798. The correlations between them were higher than other vegetation indices.
{"title":"Vegetation water content estimation using bi-inverted Gaussian model","authors":"L. Xuan, Z. Ye, Junping Zhang","doi":"10.1109/WHISPERS.2016.8071741","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071741","url":null,"abstract":"This paper presented a new approach called bi-inverted Gaussian model to calculated the diagnostic characteristic parameters of vegetation spectral. And used the parameters calculated from Hyperion image to make water content mapping. Using laboratory experiment measuring data, the relationships between absorption depth and the vegetation water content (VWC) were calculated. between absorption depth and VWC was 0.868 and the RMSE was 0.798. The correlations between them were higher than other vegetation indices.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125896823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071768
Yuanchao Su, Xu Sun, Lianru Gao, Jun Yu Li, Bing Zhang
Endmember extraction is a key step in hyperspectral unmixing. This paper proposes a new endmember extraction framework based on the swarm intelligence algorithm. We adopt a discrete structure because pixels exist within a discrete frame. Traditional swarm intelligence algorithms produce stacked solutions based on similar endmembers in the same class. We introduce a “distance” factor into the objective function to limit the number of endmembers per class. We then propose three endmember extraction methods based on the artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms. Experiments with both simulated and actual hyperspectral image data indicate that the proposed framework can significantly improve the accuracy of endmember extraction.
{"title":"Improved discrete swarm intelligence algorithms for endmember extraction in hyperspectral remote sensing image","authors":"Yuanchao Su, Xu Sun, Lianru Gao, Jun Yu Li, Bing Zhang","doi":"10.1109/WHISPERS.2016.8071768","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071768","url":null,"abstract":"Endmember extraction is a key step in hyperspectral unmixing. This paper proposes a new endmember extraction framework based on the swarm intelligence algorithm. We adopt a discrete structure because pixels exist within a discrete frame. Traditional swarm intelligence algorithms produce stacked solutions based on similar endmembers in the same class. We introduce a “distance” factor into the objective function to limit the number of endmembers per class. We then propose three endmember extraction methods based on the artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms. Experiments with both simulated and actual hyperspectral image data indicate that the proposed framework can significantly improve the accuracy of endmember extraction.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124569590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071656
Pedram Ghamisi, R. Souza, J. Benediktsson, Xiaoxiang Zhu, L. Rittner, R. Lotufo
In this paper, a novel approach is proposed for the spectral-spatial classification of hyperspectral images. The proposed classification approach is based on a novel filtering technique, here entitled as extended extinction profile (EEP). The proposed classification approach is applied on two well-known data sets: Pavia University and Indian Pines; and the obtained results have been compared with one of the strongest filtering approaches in the literature named extended attribute profile (EAP). Results confirm that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images.
{"title":"Extended extinction profile for the classification of hyperspectral images","authors":"Pedram Ghamisi, R. Souza, J. Benediktsson, Xiaoxiang Zhu, L. Rittner, R. Lotufo","doi":"10.1109/WHISPERS.2016.8071656","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071656","url":null,"abstract":"In this paper, a novel approach is proposed for the spectral-spatial classification of hyperspectral images. The proposed classification approach is based on a novel filtering technique, here entitled as extended extinction profile (EEP). The proposed classification approach is applied on two well-known data sets: Pavia University and Indian Pines; and the obtained results have been compared with one of the strongest filtering approaches in the literature named extended attribute profile (EAP). Results confirm that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121098136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071700
M. Fahad, Mingyi He, Yifan Zhang
There are two target detection algorithms which are commonly used in various applications. Both of them work on a related linear process, which makes them intensely related. This paper suggests a hyperspectral target detection algorithm which is a combination of CEM (Constrained Energy Minimization) and RXD (Reed-Xiaoli detector) algorithms to employ the advantages of both approaches to improve detection performance. The comparison of different target detection algorithms are performed by Receiver Operating Characteristic (ROC) Curves. The experimental result shows that this combination can efficiently improves the detection performance.
{"title":"Combination of CEM & RXD for target detection in hyperspectral images","authors":"M. Fahad, Mingyi He, Yifan Zhang","doi":"10.1109/WHISPERS.2016.8071700","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071700","url":null,"abstract":"There are two target detection algorithms which are commonly used in various applications. Both of them work on a related linear process, which makes them intensely related. This paper suggests a hyperspectral target detection algorithm which is a combination of CEM (Constrained Energy Minimization) and RXD (Reed-Xiaoli detector) algorithms to employ the advantages of both approaches to improve detection performance. The comparison of different target detection algorithms are performed by Receiver Operating Characteristic (ROC) Curves. The experimental result shows that this combination can efficiently improves the detection performance.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071714
Mohammed Q. Alkhatib, M. Velez-Reyes
This paper presents a visual exploratory analysis of an AVIRIS hyperspectral image to understand the interactions between the spatial and spectral domains in hyperspectral unmixing. We show how the global data cloud may not be convex due to spatial constraints on the distribution of the materials in the scene. Furthermore, we show that by segmenting the data cloud in feature space into piecewise convex segments, we can analyze individual segments and extract endmembers that better capture local structures compared to methods that look at the global cloud. Challenges remain as to how to do the cloud segmentation using machine-based approaches. However, experimental results point to the use of segmentation as a way to address the problem.
{"title":"Understanding spatial-spectral domain interactions in hyperspectral unmixing using exploratory data analysis","authors":"Mohammed Q. Alkhatib, M. Velez-Reyes","doi":"10.1109/WHISPERS.2016.8071714","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071714","url":null,"abstract":"This paper presents a visual exploratory analysis of an AVIRIS hyperspectral image to understand the interactions between the spatial and spectral domains in hyperspectral unmixing. We show how the global data cloud may not be convex due to spatial constraints on the distribution of the materials in the scene. Furthermore, we show that by segmenting the data cloud in feature space into piecewise convex segments, we can analyze individual segments and extract endmembers that better capture local structures compared to methods that look at the global cloud. Challenges remain as to how to do the cloud segmentation using machine-based approaches. However, experimental results point to the use of segmentation as a way to address the problem.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127594903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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