Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data
Roozbeh Rajabi,Hassan Ghassemian +1 more
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TLDR
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.read more
Citations
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Journal ArticleDOI
Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing
TL;DR: In this article, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances.
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Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing
TL;DR: An adaptive graph to regularize a multilayer NMF (AGMLNMF) model for hyperspectral unmixing, where a graph is constructed based on the probabilities between neighbors, which enables the optimal neighborhood be automatically determined.
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An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data
TL;DR: In this paper, the authors proposed a temporal mixture analysis (TMA) based method for estimating the ISA fraction at a large scale, which is a variant of spectral mixture analysis that makes full use of the phenological information of different land cover types.
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Enhancing Hyperspectral Endmember Extraction Using Clustering and Oversegmentation-Based Preprocessing
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.
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Target/Background Classification Regularized Nonnegative Matrix Factorization for Fluorescence Unmixing
Binjie Qin,Chen Hu,Shaosen Huang +2 more
TL;DR: Improved normalized cut is proposed to automatically classify all multispectral pixels into target fluorophores and background AF groups and shows the superiority of the proposed algorithm with respect to other state-of-the-art approaches.
References
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Journal ArticleDOI
Vertex component analysis: a fast algorithm to unmix hyperspectral data
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.
Journal ArticleDOI
Graph Regularized Nonnegative Matrix Factorization for Data Representation
TL;DR: In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
Journal ArticleDOI
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
D.C. Heinz,Chein-I-Chang +1 more
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.
Journal ArticleDOI
Parameter-less Auto-weighted multiple graph regularized Nonnegative Matrix Factorization for data representation
TL;DR: In GNMF, an affinity graph is constructed to encode the geometrical information and a matrix factorization is sought, which respects the graph structure, and the empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.
Journal ArticleDOI
Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization
Lidan Miao,Hairong Qi +1 more
TL;DR: A novel method without the pure-pixel assumption is presented, referred to as the minimum volume constrained nonnegative matrix factorization (MVC-NMF), for unsupervised endmember extraction from highly mixed image data, which outperforms several other advanced endmember detection approaches.
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