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2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021 In this article the author reviews José Bioucas-Dias' key contributions to hyperspectral unmixing (HU), in memory of him as an influential scholar and for his many beautiful ideas introduced to the hyperspectral community. Our story will start with vertex component analysis (VCA) -- one of the most celebrated HU algorithms, with more than 2,000 Google ...
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Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
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ASSESSING AND COMPARING THE PERFORMANCE OF ENDMEMBER EXTRACTION METHODS IN MULTIPLE CHANGE DETECTION USING HYPERSPECTRAL DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Endmember extraction is a process to identify the hidden pure source signals from the mixture. Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral ...
H. Jafarzadeh, M. Hasanlou
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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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Benchmark for Hyperspectral Unmixing Algorithm Evaluation
Informatica, 2023 Over the past decades, many methods have been proposed to solve the linear or nonlinear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few
Vytautas Paura +1 more
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Nonlinear Hyperspectral Unmixing With Graphical Models [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2019 In optical remote sensing, phenomena such as multiple scattering, shadowing, and spatial neighbor effects generate spectral reflectances that are nonlinear mixtures of the reflectances of the surface materials. Using hyperspectral images, the obtained spectral reflectances can be unmixed. We present a general method for creating nonlinear mixing models,
Rob Heylen +4 more
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Remote Sensing, 2020 This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network.
Yiliang Zeng +3 more
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DLR HySU—A Benchmark Dataset for Spectral Unmixing
Remote Sensing, 2021 Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers.
Daniele Cerra +10 more
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Hyperspectral Unmixing Hierarchies [PDF]
CoRRUnmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth requirements, can accommodate mixed pixels, and is closely tied to light propagation, it is a uniquely powerful tool for analyzing hyperspectral images.
Joseph L. Garrett +6 more
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Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging
IEEE Access, 2019 Sparse hyperspectral unmixing aims at finding the sparse fractional abundance vector of a spectral signature present in a mixed pixel. However, there are several types of noise present in the hyperspectral images.
Taner Ince, Tugcan Dundar
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