Results 1 to 10 of about 50 (50)
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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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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Double Regression‐Based Sparse Unmixing for Hyperspectral Images [PDF]
Journal of Sensors, 2021 Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity.
Shuaiyang Zhang +5 more
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Approximate Sparse Regularized Hyperspectral Unmixing [PDF]
Mathematical Problems in Engineering, 2014 Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing ...
Chengzhi Deng +6 more
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Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2014 Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
Marian-Daniel Iordache +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.
Zhu, Feiyun +4 more
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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 abundances. However, most existing spatial sparse unmixing algorithms are sensitive to noise and produce
Fan Li +5 more
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Recent developments in sparse hyperspectral unmixing [PDF]
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010 This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well-known endmember extraction techniques widely available in the literature.
Marian-Daniel Iordache +2 more
openaire +1 more source