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Robust Double Spatial Regularization Sparse Hyperspectral Unmixing [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 With the help of endmember spectral library, sparse unmixing techniques have been successfully applied to hyperspectral image interpretation. The inclusion of spatial information in the sparse unmixing significantly improves the resulting fractional ...
Fan Li +5 more
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Manifold regularization for sparse unmixing of hyperspectral images. [PDF]
Springerplus, 2016 Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an ...
Liu J, Zhang C, Zhang J, Li H, Gao Y.
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Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints [PDF]
Scientific ReportsHyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite.
Chenguang Xu
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Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery
Remote Sensing, 2023 Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries.
Chengzhi Deng +7 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Sparse unmixing with a semisupervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit ...
Hongjun Su +3 more
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SUnAA: Sparse Unmixing Using Archetypal Analysis
IEEE Geoscience and Remote Sensing Letters, 2023 This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of interest are a convex combination of endmembers provided by a spectral library and that the number of endmembers of interest is known.
Rasti, Behnood +3 more
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Sparse Unmixing using Deep Convolutional Networks
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022 Abstract: This paper proposes a sparse unmixing technique using a convolutional neural network (SUnCNN). We reformulate the sparse unmixing problem into an optimization over the parameters of a convolutional network. Relying on a spectral library, the deep network learns in an unsuper-vised manner a mapping from a fixed input to the sparse abundances ...
Rasti, Behnood +2 more
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Proceedings of the 39th International Symposium on Symbolic and Algebraic Computation, 2014 Toric (or sparse) elimination theory is a framework developped during the last decades to exploit monomial structures in systems of Laurent polynomials. Roughly speaking, this amounts to computing in a \emph{semigroup algebra}, \emph{i.e.} an algebra generated by a subset of Laurent monomials. In order to solve symbolically sparse systems, we introduce
Faugère, Jean-Charles +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse unmixing methods have been extensively studied as a popular topic in hyperspectral image analysis for several years. Fundamental model-based unmixing problems can be better reformulated by exploiting sparse constraints in different forms. Gradient-
Yapeng Miao, Bin Yang
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Sparse Unmixing of Hyperspectral Data [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2011 Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer.
Marian-Daniel Iordache +2 more
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