Results 81 to 90 of about 1,407 (163)
Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing. [PDF]
Jia X, Guo B.
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Updated Homogeneity Criteria Based Low-Dimensional Representation for Hyperspectral Unmixing
Superpixel-based approaches have been proposed for hyperspectral unmixing. The basic assumption of this approach is that the superpixel over-segmentation segments the image into small homogeneous areas.
Jiarui Yi, Huiyi Gao
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Hyperspectral unmixing has attracted increasing attention in remote sensing applications. Unfortunately, significant unmixing residuals often arise from the coupled nonlinear mixing effects and spectral variability (SV), bringing challenges for reliably ...
Yi Zhao, Bin Yang
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In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang +3 more
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Contributions to Hyperspectral Unmixing
Le démelangeage spectral est un domaine de recherche actif qui trouve des applications dans des domaines variés comme la télédétection, le traitement des signaux audio ou la chimie. Dans le contexte des capteurs hyper spectraux, les images acquises sont souvent de faible résolution spatiale, principalement à cause des limites technologiques liées aux ...
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An Open Evaluation of Hyperspectral Unmixing Strategies for EDS Analysis. [PDF]
Taillon JA.
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A Dye-Free Analog to Retinal Angiography Using Hyperspectral Unmixing to Retrieve Oxyhemoglobin Abundance. [PDF]
Dwight JG +3 more
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Spatial-Channel Multiscale Transformer Network for Hyperspectral Unmixing. [PDF]
Sun H, Cao Q, Meng F, Xu J, Cheng M.
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Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation. [PDF]
Alshahrani AA, Bchir O, Ben Ismail MM.
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Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders. [PDF]
Georgiev D +6 more
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