Hyperspectral data unmixing using constrained semi-NMF and PCA transform
15 May 2012-pp 1523-1527
TL;DR: In this article, a new approach for the unmixing of hyperspectral data using the semi-nonnegative matrix factor (semi-NMF) and principal component analysis (PCA) is proposed that solves the problem of correlation between bands and decrease execution time of algorithm.
Abstract: One of problems that have been not considered in unmixing process of hyperspectral is the correlation between bands. This correlation makes difficult the unmixing of spectral signatures of different materials. Furthermore, the large number of spectral bands extends the execution time of the unmixing process. In this paper, a new approach for the unmixing of hyperspectral data using the semi-Nonnegative Matrix Factor (semi-NMF) and Principal Component Analysis (PCA) is proposed that solves the problem of correlation between bands and decrease execution time of algorithm. The proposed approach uses from PCA of data in the unmixing process instead of original data. Using this linear transformation, the images are mapped to the uncorrelated space. Uncorrelated images make more efficient the unmixing process. In order to overcome the problem of non-uniqueness solution that is caused by the non-convex cost function, the smoothness and sparseness constraints are introduced to the semi-NMF. In addition to its high accuracy, the proposed method increases the speed of the unmixing process. The experimental results show excellence of the proposed approach in comparison of other methods.
Citations
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TL;DR: An appropriate clustering and oversegmentation-based preprocessing (COPP) by greatly benefiting from the integration of spatial and spectral information is proposed and a novel top-down overse segmentation (TDOS) algorithm is developed which can recognize small oversegments with high spatial correlation.
Abstract: Spectral mixture analysis (SMA) is an effective tool in recognition of unique spectral signatures of materials called endmembers and estimating their percentage of existence (abundance fractions). Most approaches designed in endmember extraction process are established by applying the spectral information of the dataset and, thus, tend to neglect the existing spatial correlation between adjacent pixels. Although several preprocessing modules have been developed by incorporating both spatial and spectral properties prior to spectral-based endmember extraction algorithms (EEs), they still encounter several challenges. Hence, in this paper, we propose an appropriate clustering and oversegmentation-based preprocessing (COPP) by greatly benefiting from the integration of spatial and spectral information. Moreover, a novel top-down oversegmentation (TDOS) algorithm is developed which can recognize small oversegments with high spatial correlation. Our scheme removes oversegments located at spatial border of cluster regions. Average spectral vectors of determined spatially homogenous oversegments are considered so that their spectral purity scores are calculated. COPP identifies spatially homogenous zones with the greatest spectral purity scores. Pixels of these regions are more likely to be adopted as endmembers by means of subsequent EEs. COPP can take advantage of degrading local spectral variability and noise power. The main contribution of this paper is the enhanced computational performance of EE as well as the precise reconstruction of the original hyperspectral scene besides its appropriate recognition of endmembers’ spectral signatures. The effectiveness of our design and its validation are appraised with the state-of-the-art strategies on a synthetic and AVIRIS real hyperspectral datasets.
20 citations
TL;DR: In this paper, the graph regularized NMF (GNMF) method combined with sparseness constraint has been used to decompose mixed pixels in hyperspectral imagery, which preserves the geometrical structure of data while representing it in low dimensional space.
Abstract: Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to non negativity constraint on abundance fractions, non negative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized NMF (GNMF) method combined with sparseness constraint to decompose mixed pixels in hyperspectral imagery. This method preserves the geometrical structure of data while representing it in low dimensional space. Adaptive regularization parameter based on temperature schedule in simulated annealing method also has been used in this paper for the sparseness term. Proposed algorithm is applied on synthetic and real datasets. Synthetic data is generated based on endmembers from USGS spectral library. AVIRIS Cuprite dataset is used as real dataset for evaluation of proposed method. Results are quantified based on spectral angle distance (SAD) and abundance angle distance (AAD) measures. Results in comparison with other methods show that the proposed method can unmix data more effectively. Specifically for the Cuprite dataset, performance of the proposed method is approximately 10 % better than the VCA and Sparse NMF in terms of root mean square of SAD.
20 citations
Cites methods from "Hyperspectral data unmixing using c..."
...Several algorithms proposed in the literature based on NMF (Miao and Qi 2007; Yang et al. 2011; Alizadeh and Ghassemian 2012)....
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26 Jul 2015
TL;DR: A novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) is proposed by removing mixed pixels located at spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spectrally pure pixels thorough them so that the best pure pixels are adopted for the next EE stage.
Abstract: Most algorithms applied for extracting endmembers utilize spectral content of pixels with inattentive to spatial arrangement between them. Recently Spatial Spectral Preprocessor (SSPP) has been proposed for solving this problem. In this paper, we propose a novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) by removing mixed pixels located at spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spectrally pure pixels thorough them so that the best pure pixels are adopted for the next EE stage. The performance of our method is appraised on a synthetic image derived by Rterrain HYDICE dataset and USGS library from the viewpoints of RMSE reconstruction, average minimum SAD and total processing time. Results relatively outperform the state-of-the-art techniques especially in low signal to noise ratio.
8 citations
Cites methods from "Hyperspectral data unmixing using c..."
...For comparison of CSBRP with the other mentioned methods, we use two important metrics that are Spectral Angle Distance (SAD)–based error and Root Mean Square Error (RMSE)–based error of reconstructed image [9,11-16]....
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TL;DR: In this article, the graph regularized NMF (GNMF) method combined with sparseness constraint has been used to decompose mixed pixels in hyperspectral imagery, which preserves the geometrical structure of data while representing it in low dimensional space.
Abstract: Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized NMF (GNMF) method combined with sparseness constraint to decompose mixed pixels in hyperspectral imagery. This method preserves the geometrical structure of data while representing it in low dimensional space. Adaptive regularization parameter based on temperature schedule in simulated annealing method also has been used in this paper for the sparseness term. Proposed algorithm is applied on synthetic and real datasets. Synthetic data is generated based on endmembers from USGS spectral library. AVIRIS Cuprite dataset is used as real dataset for evaluation of proposed method. Results are quantified based on spectral angle distance (SAD) and abundance angle distance (AAD) measures. Results in comparison with other methods show that the proposed method can unmix data more effectively. Specifically for the Cuprite dataset, performance of the proposed method is approximately 10% better than the VCA and Sparse NMF in terms of root mean square of SAD.
7 citations
Patent•
30 Jul 2014
TL;DR: In this article, a method for remote sensing image change detection based on the Semi-NMF algorithm is proposed, in which each feature vector based on neighborhood information and corresponding to one pixel in the difference image is obtained through PCA and a feature matrix X is established through the feature vectors.
Abstract: The invention discloses a method for remote sensing image change detection based on the Semi-NMF. A processed object simultaneously comprises an optical remote sensing image and a synthetic aperture radar image. The method mainly solves the problems that when a strong change region is obtained through an existing method for remote sensing image change detection, weak and small change regions can not be detected, and more detail and marginal information can not be effectively kept. The realization process of the method comprises the steps that (1) a difference image is generated according to the type of a remote sensing image; (2) each feature vector based on neighborhood information and corresponding to one pixel in the difference image is obtained through PCA and a feature matrix X is established through the feature vectors; (3) the Semi-NMF algorithm is conducted on the feature matrix X, so that the feature matrix X is dissolved to a basis matrix F and a coefficient matrix G through iterative operation; (4) according to the coefficient matrix, a change type omega c and an unchanged type omega u are judged, the soft clustering function is achieved, and a binary change detection result is obtained. According to the method, loss of marginal information is reduced, the strong, weak and small change regions can be detected at the same time, the total error rate is reduced, more detail information is kept, and the change result is effectively obtained.
6 citations
References
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TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Abstract: 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.
2,422 citations
Journal Article•
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TL;DR: The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures and the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels.
Abstract: Spectral unmixing using hyperspectral data represents a significant step in the evolution of remote decompositional analysis that began with multispectral sensing. It is a consequence of collecting data in greater and greater quantities and the desire to extract more detailed information about the material composition of surfaces. Linear mixing is the key assumption that has permitted well-known algorithms to be adapted to the unmixing problem. In fact, the resemblance of the linear mixing model to system models in other areas has permitted a significant legacy of algorithms from a wide range of applications to be adapted to unmixing. However, it is still unclear whether the assumption of linearity is sufficient to model the mixing process in every application of interest. It is clear, however, that the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels. The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures.
1,917 citations
TL;DR: The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification, where no closed form can be derived for this method and an efficient algorithm is developed to yield optimal solutions.
Abstract: 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 case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner.
1,676 citations
27 Oct 1999
TL;DR: A method based upon the geometry of convex sets is proposed to find a unique set ofpurest pixels in an image, based on the fact that in N spectral dimensions, the N-volume contained by a simplex formed of the purest pixels is larger than any other volume formed from any other combination of pixels.
Abstract: The analysis of hyperspectral data sets requires the determination of certain basis spectra called 'end-members.' Once these spectra are found, the image cube can be 'unmixed' into the fractional abundance of each material in each pixel. There exist several techniques for accomplishing the determination of the end-members, most of which involve the intervention of a trained geologist. Often these-end-members are assumed to be present in the image, in the form of pure, or unmixed, pixels. In this paper a method based upon the geometry of convex sets is proposed to find a unique set of purest pixels in an image. The technique is based on the fact that in N spectral dimensions, the N-volume contained by a simplex formed of the purest pixels is larger than any other volume formed from any other combination of pixels. The algorithm works by 'inflating' a simplex inside the data, beginning with a random set of pixels. For each pixel and each end-member, the end-member is replaced with the spectrum of the pixel and the volume is recalculated. If it increases, the spectrum of the new pixel replaces that end-member. This procedure is repeated until no more replacements are done. This algorithm successfully derives end-members in a synthetic data set, and appears robust with less than perfect data. Spectral end-members have been extracted for the AVIRIS Cuprite data set which closely match reference spectra, and resulting abundance maps match published mineral maps.
1,284 citations
25 Oct 1993
TL;DR: In this paper, a linear mixing model and a set of hypothesized end-member spectra are used to estimate the fractional abundance patterns of the various materials occurring within the imaged area.
Abstract: Spectral mixture analysis, or unmixing, has proven to be a useful tool in the semi-quantitative interpretation of AVIRIS data. Using a linear mixing model and a set of hypothesized endmember spectra, unmixing seeks to estimate the fractional abundance patterns of the various materials occurring within the imaged area. However, the validity and accuracy of the unmixing rest heavily on the 'user-supplied' set of endmember spectra. Current methods for emdmember determination are the weak link in the unmixing chain.
692 citations