Results 41 to 50 of about 423,442 (225)
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation
Remote Sensing, 2017 Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different ...
Chang Li +5 more
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Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing [PDF]
, 2015 This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the ...
Altmann, Yoann +2 more
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Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing
IEEE Access, 2019 Sparse unmixing has attracted much attention in recent years. It aims at estimating the fractional abundances of pure spectral signatures in mixed pixels in hyperspectral images.
Jie Huang +3 more
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Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images.
Pan Zheng, Hongjun Su, Qian Du
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Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery
Remote Sensing, 2018 Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the ...
Ruyi Feng, Lizhe Wang, Yanfei Zhong
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Hyperspectral Unmixing Via Nonconvex Sparse and Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid the bottleneck problems associated with the absence of pure pixels and the estimation of the number of endmembers in hyperspectral scenes.
Hongwei Han +7 more
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IEEE Access, 2021 Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation–based unmixing (CRU) for hyperspectral images is proposed.
Jing Wang
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Spatial–Spectral Multiscale Sparse Unmixing for Hyperspectral Images
IEEE Geoscience and Remote Sensing Letters, 2023 We propose a simple yet efficient sparse unmixing method for hyperspectral images. It exploits the spatial and spectral properties of hyperspectral images by designing a new regularization informed by multiscale analysis.
Taner Ince, Nicolas Dobigeon
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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
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Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2021 Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the product of endmembers and their corresponding fractional abundances, which has been widely used in hyperspectral imagery analysis.
Ge Zhang +6 more
semanticscholar +1 more source