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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, Yanfei Zhong, Lizhe Wang
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Robust Double Spatial Regularization Sparse Hyperspectral Unmixing
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 ...
Chengzhi Deng +2 more
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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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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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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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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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Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection
Remote Sensing, 2021 Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.
Genping Zhao +4 more
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Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 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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