Results 11 to 20 of about 1,157 (179)
Bilateral Joint-Sparse Regression for Hyperspectral Unmixing [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse hyperspectral unmixing has been a hot topic in recent years. Joint sparsity assumes that each pixel in a small neighborhood of hyperspectral images (HSIs) is composed of the same endmembers, which results in a few nonzero rows in the abundance ...
Jie Huang +4 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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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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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, 2023 With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise.
Yao Liang +4 more
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Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing refers to the process of obtaining endmembers and abundance vectors through linear or nonlinear models. The traditional linear unmixing model assumes that each mixed pixel can be represented by a linear combination of endmembers ...
Lulu Wan +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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Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery
Remote Sensing, 2017 Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional ...
Ruyi Feng +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 ...
Behnood Rasti +2 more
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Structured Sparse Method for Hyperspectral Unmixing [PDF]
ISPRS Journal of Photogrammetry and Remote Sensing, 2014 Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data.
Feiyun Zhu +4 more
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