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Dynamical spectral unmixing of multitemporal hyperspectral images [PDF]
IEEE Transactions on Image Processing, 2015 In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant.
Chanussot, Jocelyn +2 more
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DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in
S. Khoshsokhan, R. Rajabi, H. Zayyani
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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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Blind and endmember guided autoencoder model for unmixing the absorbance spectra of phytoplankton pigments [PDF]
Scientific ReportsHyperspectral sensing of phytoplankton, free-living microscopic photosynthetic organisms, offers a comprehensive and scalable method for assessing water quality and monitoring changes in aquatic ecosystems.
Pritish Naik +2 more
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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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Hyperspectral Unmixing Using Transformer Network
IEEE Transactions on Geoscience and Remote Sensing, 2022 Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness
Preetam Ghosh +4 more
openaire +4 more sources
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 Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable ...
Danni Jin, Bin Yang
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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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