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Endmember-Free Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingUnmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu +6 more
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Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising
Remote Sensing, 2023 The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms.
Risheng Huang +4 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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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral unmixing. Totally an AE performs well in hyperspectral unmixing through automatically learning low-dimensional embedding and reconstructing data.
Xiao Chen +5 more
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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 ...
Fan Li +5 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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A New Deep Convolutional Network for Effective Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms ...
Xuanwen Tao +7 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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How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
Remote Sensing, 2020 Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti +3 more
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Projection-Based NMF for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015 As a widely concerned research topic, many advanced algorithms have been proposed for hyperspectral unmixing. However, they may fail to accurately identify endmember signatures when coming across insufficient spatial resolution. To deal with this problem, an algorithm based on semisupervised linear sparse regression is proposed, in which unmixing ...
Yuan Yuan, Yachuang Feng, Xiaoqiang Lu
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