Results 1 - 10 of 187

Vertex component analysis: A fast algorithm to unmix hyperspectral data

by José M. P. Nascimento, José M. Bioucas Dias - 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 ..."
Abstract - Cited by 193 (16 self) - Add to MetaCart
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 state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery

by Daniel C. Heinz, Chein-i Chang - IEEE TRANS. ON GEOSCIENCE AND REMOTE SENSING , 2001
"... Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the l ..."
Abstract - Cited by 169 (4 self) - Add to MetaCart
Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this

Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches

by José M. Bioucas-dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot - 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 ..."
Abstract - Cited by 103 (34 self) - Add to MetaCart
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, ill-posed

Spatial/spectral endmember extraction by multidimensional morphological operations

by Antonio Plaza, Pablo Martínez, Rosa Pérez, Javier Plaza - IEEE Transactions on Geoscience and Remote Sensing
"... Abstract—Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of t ..."
Abstract - Cited by 103 (48 self) - Add to MetaCart
Abstract—Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data. Index Terms—Automated endmember extraction, mathematical morphology, morphological eccentricity index, multidimensional analysis, spatial/spectral integration, spectral mixture model. I.
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THE HYBRID STEEPEST DESCENT METHOD FOR THE VARIATIONAL INEQUALITY PROBLEM OVER THE INTERSECTION OF FIXED POINT SETS OF NONEXPANSIVE MAPPINGS

by Isao Yamada , 2001
"... This paper presents a simple algorithmic solution to the variational inequality prob-lem defined over the nonempty intersection of multiple fixed point sets of nonexpansive mappings in a real Hilbert space. The algorithmic solution is named the hybrid steepest descent method, because it is construct ..."
Abstract - Cited by 86 (6 self) - Add to MetaCart
This paper presents a simple algorithmic solution to the variational inequality prob-lem defined over the nonempty intersection of multiple fixed point sets of nonexpansive mappings in a real Hilbert space. The algorithmic solution is named the hybrid steepest descent method, because it is constructed by blending important ideas in the steepest de-scent method and in the fixed point theory, and generates a sequence converging strongly to the solution of the problem. The remarkable applicability of this method to the convexly constrained generalized pseudoinverse problem as well as to the convex feasibility problem is demonstrated by constructing nonexpansive mappings whose fixed point sets are the feasible sets of the problems.

Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery

by Chein-i Chang, Senior Member, Qian Du - 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 ..."
Abstract - Cited by 83 (10 self) - Add to MetaCart
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 theory-based thresholding methods are developed to determine the VD of hyperspectral imagery, where eigenvalues
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Does independent component analysis play a role in unmixing hyperspectral data

by José M. P. Nascimento, José M. Bioucas Dias - IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2005
"... Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) ..."
Abstract - Cited by 61 (13 self) - Add to MetaCart
Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2) sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Hyperspectral Image Processing for Automatic Target Detection Applications

by Dimitris Manolakis, David Marden, Gary A. Shaw , 2003
"... This article presents an overview of the theoretical and practical issues associated with the development, analysis, and application of detection algorithms to exploit hyperspectral imaging data. We focus on techniques that exploit spectral information exclusively to make decisions regarding the ty ..."
Abstract - Cited by 61 (0 self) - Add to MetaCart
This article presents an overview of the theoretical and practical issues associated with the development, analysis, and application of detection algorithms to exploit hyperspectral imaging data. We focus on techniques that exploit spectral information exclusively to make decisions regarding the type of each pixel—target or nontarget—on a pixel-by-pixel basis in an image. First we describe the fundamental structure of the hyperspectral data and explain how these data influence the signal models used for the development and theoretical analysis of detection algorithms. Next we discuss the approach used to derive detection algorithms, the performance metrics necessary for the evaluation of these algorithms, and a taxonomy that presents the various algorithms in a systematic manner. We derive the basic algorithms in each family, explain how they work, and provide results for their theoretical performance. We conclude with empirical results that use hyperspectral imaging data from the HYDICE and Hyperion sensors to illustrate the operation and performance of various detectors.

Least squares subspace projection approach to mixed pixel classi_ation for hyperspectral images

by Chein-i Chang, Senior Member, Xiao-li Zhao, Mark L. G. Althouse, Jeng Jong Pan - IEEE Transection on Geoscience Remote Sensing , 1998
"... Abstract—An orthogonal subspace projection (OSP) method using linear mixture modeling was recently explored in hyper-spectral image classification and has shown promise in signature detection, discrimination, and classification. In this paper, the OSP is revisited and extended by three unconstrained ..."
Abstract - Cited by 46 (4 self) - Add to MetaCart
Abstract—An orthogonal subspace projection (OSP) method using linear mixture modeling was recently explored in hyper-spectral image classification and has shown promise in signature detection, discrimination, and classification. In this paper, the OSP is revisited and extended by three unconstrained least squares subspace projection approaches, called signature space OSP, target signature space OSP, and oblique subspace pro-jection, where the abundances of spectral signatures are not known a priori but need to be estimated, a situation to which the OSP cannot be directly applied. The proposed three subspace projection methods can be used not only to estimate signature abundance, but also to classify a target signature at subpixel scale so as to achieve subpixel detection. As a result, they can be viewed as a posteriori OSP as opposed to OSP, which can be thought of as a priori OSP. In order to evaluate these three approaches, their associated least squares estimation errors are cast as a signal detection problem in the framework of the Ney-man–Pearson detection theory so that the effectiveness of their generated classifiers can be measured by characteristics (ROC) analysis. All results are demonstrated by computer simulations and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data.
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Multispectral and hyper-spectral image analysis with convex cones

by Agustin Ifarraguerri, Student Member, Chein-i Chang, Senior Member - 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 quan-tities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of non ..."
Abstract - Cited by 43 (0 self) - Add to MetaCart
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 quan-tities 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, hyperspec-tral digital imagery collection experiment (HYDICE), hyperspec-tral image, multispectral image, unmixing. I.
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