Results 11 to 20 of about 77,969 (223)
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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Deep‐Learning‐Driven High‐Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon‐Limited Conditions [PDF]
Advanced Science, Volume 13, Issue 59, 23 October 2026.Highly overlapping fluorescent signals become distinguishable in photon‐limited living organisms via advanced imaging with intelligent reconstruction. The resulting in vivo hyperspectral imaging capability reveals nanoplastic uptake and circulation in live zebrafish, providing a new approach for studying complex biological and environmental processes ...
Renjian Li +11 more
wiley +2 more sources
Phasor‐Based Spatio‐Spectral Segmentation for Hyperspectral Fluorescence Microscopy [PDF]
Journal of Biophotonics, Volume 19, Issue 9, September 2026.Spatially‐corrected density peak clustering (SC‐DPC) enables high‐accuracy segmentation of hyperspectral fluorescence microscopy data in the presence of significant spectral and spatial overlap between fluorophores. Validated on retinal imaging, SC‐DPC effectively separates overlapping fluorophore spectra in photon‐starved conditions, offering superior
Maciej Barna +4 more
wiley +2 more sources
Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation.
Lei Tong +4 more
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Transformer for Multitemporal Hyperspectral Image Unmixing [PDF]
IEEE Transactions on Image ProcessingMultitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmixing, the multitemporal approach demands comprehensive consideration of information across different phases, rendering it a greater challenge.
Hang Li +5 more
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Catadioptric hyperspectral imaging, an unmixing approach [PDF]
IET Computer Vision, 2020 Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications.
Özışık Başkurt, Didem +2 more
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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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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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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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