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277
Vertex component analysis: A fast algorithm to unmix hyperspectral data
 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 2005
"... Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for ..."
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Cited by 193 (16 self)
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Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: 1) the endmembers are the vertices of a simplex and 2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with stateoftheart methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach
 IEEE Transactions on Geoscience and Remote Sensing
, 1994
"... AbstructMost applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volumeldimensionality, without loss of critical information, so that it can be proc ..."
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Cited by 187 (16 self)
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AbstructMost applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volumeldimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst. In this paper, we describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signaltonoise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels. I.
Hyperspectral unmixing overview: Geometrical, statistical, and sparse regressionbased approaches
 IEEE J. SEL. TOPICS APPL. EARTH OBSERV. REMOTE SENS
, 2012
"... Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher sp ..."
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Cited by 103 (34 self)
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Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, illposed
Hyperspectral subspace identification
 IEEE Trans. Geosci. Remote Sens
, 2008
"... Abstract—Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performanc ..."
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Cited by 83 (23 self)
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Abstract—Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square errorbased approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. Stateoftheart performance of the proposed method is illustrated by using simulated and real hyperspectral images. Index Terms—Dimensionality reduction, hyperspectral imagery, hyperspectral signal subspace identification by minimum error (HySime), hyperspectral unmixing, linear mixture, minimum mean square error (mse), subspace identification. I.
Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery
 IEEE Transactions on Geoscience and Remote Sensing
, 2004
"... Abstract—With very high spectral resolution, hyperspectral sensors can now uncover many unknown signal sources which cannot be identified by visual inspection or a priori. In order to account for such unknown signal sources, we introduce a new definition, referred to as virtual dimensionality (VD) i ..."
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Cited by 83 (10 self)
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Abstract—With very high spectral resolution, hyperspectral sensors can now uncover many unknown signal sources which cannot be identified by visual inspection or a priori. In order to account for such unknown signal sources, we introduce a new definition, referred to as virtual dimensionality (VD) in this paper. It is defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification. It is different from the commonly used intrinsic dimensionality (ID) in the sense that the signal sources are determined by the proposed VD based only on their distinct spectral properties. These signal sources may include unknown interfering sources, which cannot be identified by prior knowledge. With this new definition, three Neyman–Pearson detection theorybased thresholding methods are developed to determine the VD of hyperspectral imagery, where eigenvalues
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
"... Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown e ..."
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Cited by 67 (29 self)
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Abstract—This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for nonnegativity and fulladditivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images. Index Terms—Bayesian inference, endmember extraction, hyperspectral imagery, linear spectral unmixing, MCMC methods. I.
Distributed Clustering Using Collective Principal Component Analysis
 Knowledge and Information Systems
, 1999
"... This paper considers distributed clustering of high dimensional heterogeneous data using a distributed Principal Component Analysis (PCA) technique called the Collective PCA. It presents the Collective PCA technique that can be used independent of the clustering application. It shows a way to inte ..."
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Cited by 65 (9 self)
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This paper considers distributed clustering of high dimensional heterogeneous data using a distributed Principal Component Analysis (PCA) technique called the Collective PCA. It presents the Collective PCA technique that can be used independent of the clustering application. It shows a way to integrate the Collective PCA with a given otheshelf clustering algorithm in order to develop a distributed clustering technique. It also presents experimental results using dierent test data sets including an application for web mining.
Comparison of airborne hyperspectral data and EO1 hyperion for mineral mapping
 IEEE Trans. Geosci. Remote Sensing
, 2003
"... Abstract—Airborne hyperspectral data have been available to researchers since the early 1980s and their use for geologic applications is well documented. The launch of the National Aeronautics and Space Administration Earth Observing 1 Hyperion sensor in November 2000 marked the establishment of a ..."
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Cited by 56 (0 self)
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Abstract—Airborne hyperspectral data have been available to researchers since the early 1980s and their use for geologic applications is well documented. The launch of the National Aeronautics and Space Administration Earth Observing 1 Hyperion sensor in November 2000 marked the establishment of a test bed for spaceborne hyperspectral capabilities. Hyperion covers the 0.4–2.5 m range with 242 spectral bands at approximately 10nm spectral resolution and 30m spatial resolution. Analytical Imaging and Geophysics LLC and the Commonwealth Scientific and Industrial Research Organisation have been involved in efforts to evaluate, validate, and demonstrate Hyperions’s utility for geologic mapping in a variety of sites in the United States and around the world. Initial results over several sites with established ground truth and years of airborne hyperspectral data show that Hyperion data from the shortwave infrared spectrometer
Multispectral and hyperspectral image analysis with convex cones
 IEEE Trans. Geosci. Remote Sens
, 1999
"... Abstract—A new approach to multispectral and hyperspectral image analysis is presented. This method, called convex cone analysis (CCA), is based on the fact that some physical quantities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of non ..."
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Cited by 43 (0 self)
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Abstract—A new approach to multispectral and hyperspectral image analysis is presented. This method, called convex cone analysis (CCA), is based on the fact that some physical quantities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of nonnegative components, and they lie inside a nonnegative, convex region. The object of CCA is to find the boundary points of this region, which can be used as endmember spectra for unmixing or as target vectors for classification. To implement this concept, we find the eigenvectors of the sample spectral correlation matrix of the image. Given the number of endmembers or classes, we select as many eigenvectors corresponding to the largest eigenvalues. These eigenvectors are used as a basis to form linear combinations that have only nonnegative elements, and thus they lie inside a convex cone. The vertices of the convex cone will be those points whose spectral vector contains as many zero elements as the number of eigenvectors minus one. Accordingly, a mixed pixel can be decomposed by identifying the vertices that were used to form its spectrum. An algorithm for finding the convex cone boundaries is presented, and applications to unsupervised unmixing and classification are demonstrated with simulated data as well as experimental data from the hyperspectral digital imagery collection experiment (HYDICE). Index Terms—Classification, convex cone analysis, hyperspectral digital imagery collection experiment (HYDICE), hyperspectral image, multispectral image, unmixing. I.
Independent component analysisbased dimensionality reduction with applications in hyperspectral image analysis
 IEEE Trans. Geosci. Remote Sens
, 2006
"... Abstract—In hyperspectral image analysis, the principal components analysis (PCA) and the maximum noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR), referred to as PCADR and MNFDR, respectively. The criteria used by the PCADR and the MNFDR are data variance ..."
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Cited by 40 (1 self)
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Abstract—In hyperspectral image analysis, the principal components analysis (PCA) and the maximum noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR), referred to as PCADR and MNFDR, respectively. The criteria used by the PCADR and the MNFDR are data variance and signaltonoise ratio (SNR) which are designed to measure data secondorder statistics. This paper presents an independent component analysis (ICA) approach to DR, to be called ICADR which uses mutual information as a criterion to measure data statistical independency that exceeds secondorder statistics. As a result, the ICADR can capture information that cannot be retained or preserved by secondorder statisticsbased DR techniques. In order for the ICADR to perform effectively, the virtual dimensionality (VD) is introduced to estimate number of dimensions needed to be retained as opposed to the energy percentage that has been used by the PCADR and MNFDR to determine energies contributed by signal sources and noise. Since there is no prioritization among components generated by the ICADR due to the use of random initial projection vectors, we further develop criteria and algorithms to measure the significance of information contained in each of ICAgenerated components for component prioritization. Finally, a comparative study and analysis is conducted among the three DR techniques, PCADR, MNFDR, and ICADR in two applications, endmember extraction and data compression where the proposed ICADR has been shown to provide advantages over the PCADR and MNFDR. Index Terms—Dimensionality reduction (DR), ICADR/MNFDR, independent component analysis (ICA), maximum noise fraction (MNF), PCADR, principal components analysis (PCA), virtual dimensionality (VD). I.