Results 101 to 110 of about 77,969 (223)
Hyperspectral unmixing via deep matrix factorization
, 2017 Hyperspectral unmixing is one of the most important techniques in hyperspectral remote sensing image analysis. During the past decades, many models have been widely used in hyperspectral unmixing, such as nonnegative matrix factorization (NMF) model ...
Chuangbai Xiao +3 more
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Superpixel and low-rank double-sparse regression hyperspectral unmixing
, 2021 These are data and code for the paper "Superpixel and low-rank double-sparse regression hyperspectral ...
Shuaiyang Zang
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Hyperspectral Unmixing With Multi-Scale Convolution Attention Network
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance.
Sheng Hu, Huali Li
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping.
Yulin Xu, Xiaodong Na
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Hybrid Hyperspectral Unmixing Using Fusion Mamba and Performer Attention
IEEE AccessHyperspectral unmixing is emerging as a cutting-edge technology with applications across various areas of remote sensing. Several deep learning models, including transformer-based models, have been developed to improve unmixing accuracy.
M. Sreejam, Agilandeeswari Loganathan
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Tissue Classification of Breast Cancer by Hyperspectral Unmixing. [PDF]
Cancers (Basel), 2023 Jong LS +6 more
europepmc +1 more source
Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing. [PDF]
Sensors (Basel), 2022 Jia X, Guo B.
europepmc +1 more source
Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning
IEEE Access, 2019 Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson +2 more
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Double Regression Sparse Unmixing for Hyperspectral Image
, 2020 This file contains MATLAB code and data set, which is about “Double Regression Sparse Unmixing for Hyperspectral ...
Zhang Shuaiyang
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Nonlocal Low-Rank Nonnegative Tensor Factorization for Hyperspectral Unmixing
, 2020 Hyperspectral unmixing decomposes hyperspectral images (HSI) into a collection of constituent materials or end-members and their fractions, i.e., abundances. Nonnegative tensor factorization (NTF) has been utilized thanks to its ability of preserving all
Jun Zhou +9 more
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