A Probabilistic Joint Sparse Regression Model for Semi-Supervised Hyperspectral Unmixing

TitleA Probabilistic Joint Sparse Regression Model for Semi-Supervised Hyperspectral Unmixing
Publication TypeJournal Article
Year of Publication2017
AuthorsSeyyedsalehi, S. F., H. R. Rabiee, A. Soltani-Farani, and A. Zarezade
JournalIEEE Geoscience and Remote Sensing Letters
Volume14
Issue5
Start Page592
Pagination592-596
Type of ArticleShort Paper
ISSN Print ISSN: 1545-598X, Electronic ISSN: 1558-0571
Accession Number16823908
Keywordsjoint sparse regression, Laplacian scale mixture model, Semi-supervised hyperspectral unmixing, Spectral Libraries
AbstractSemi-supervised hyperspectral unmixing finds the ratio of spectral library members in the mixture of hyperspectral pixels to find the proportion of pure materials in a natural scene. Two main challenges are noise in observed spectral vectors and high mutual coherence of spectral libraries. To tackle these challenges, we propose a probabilistic sparse regression method for linear hyperspectral unmixing which utilizes the implicit relations of neighboring pixels. We partition the hyperspectral image into rectangular patches. Sparse coefficients of pixels in each patch are assumed to be generated from a Laplacian scale mixture model with the same latent variables. These latent variables specify the probability of existence of endmembers in the mixture of each pixel. Experiments on synthetic and real hyperspectral images illustrate the superior performance of the proposed method over alternatives.
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DOI<a href="http://dx.doi.org/10.1109/LGRS.2017.2649418&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;am
Original PublicationIEEE