Results 1 to 10 of about 1,168 (134)
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 ...
Chengzhi Deng +2 more
exaly +3 more sources
Phasor-Based Spatio-Spectral Segmentation for Hyperspectral Fluorescence Microscopy. [PDF]
J BiophotonicsSpatially‐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
Barna M +4 more
europepmc +2 more sources
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
doaj +2 more sources
Hyperspectral Unmixing with Robust Collaborative Sparse Regression
Remote Sensing, 2016 Recently, sparse unmixing (SU) of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM), which ignores the possible nonlinear ...
Xiaoguang Mei, Chang Li, Jiayi Ma
exaly +3 more sources
Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing
Remote Sensing, 2018 Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images.
Ting-Zhu Huang, Si Wang, Xi-Le Zhao
exaly +3 more sources
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
doaj +1 more source
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
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse unmixing methods have been extensively studied as a popular topic in hyperspectral image analysis for several years. Fundamental model-based unmixing problems can be better reformulated by exploiting sparse constraints in different forms. Gradient-
Yapeng Miao, Bin Yang
doaj +1 more source
Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection
Remote Sensing, 2021 Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.
Genping Zhao +4 more
doaj +1 more source
Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 more
doaj +1 more source