Results 51 to 60 of about 151 (115)
Mamba-based spatial-spectral fusion network for hyperspectral unmixing
Journal of King Saud University: Computer and Information SciencesHyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values.
Yuquan Gan, Jingtao Wei, Mengmeng Xu
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RGB‐guided hyperspectral image super‐resolution with deep progressive learning
CAAI Transactions on Intelligence Technology, Volume 9, Issue 3, Page 679-694, June 2024.Abstract Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance.
Tao Zhang +5 more
wiley +1 more source
Robust low-rank abundance matrix estimation for hyperspectral unmixing
The Journal of Engineering, 2019 Hyperspecral unmixing (HU) is one of the crucial steps of hyperspectral image (HSI) processing. The process of HU can be divided into end-member extraction and abundance estimation.
Fan Feng +4 more
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DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen +4 more
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Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2018 Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization
Zuoyu Zhang +4 more
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DNMF-AG: A Sparse Deep NMF Model with Adversarial Graph Regularization for Hyperspectral Unmixing
Remote SensingHyperspectral unmixing (HU) aims to extract constituent information from mixed pixels and is a fundamental task in hyperspectral remote sensing. Deep non-negative matrix factorization (DNMF) has recently attracted attention for HU due to its hierarchical
Kewen Qu, Xiaojuan Luo, Wenxing Bao
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Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing
MATEC Web of Conferences, 2018 As a powerful and explainable blind separation tool, non-negative matrix factorization (NMF) is attracting increasing attention in Hyperspectral Unmixing(HU).
Ning ShangBin, Zuo FengChao
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IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
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Endmember Independence and Bilateral Filtering Regularizations for Blind Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing (HU) aims to decompose the mixed pixels of a hyperspectral image into endmembers weighted by their corresponding abundances. Recently, matrix–vector nonnegative tensor factorization (MV-NTF) has been successfully applied to ...
Yang Hu, Lei Sun, Ziyang Zhang, Feng Xie
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A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing
Remote SensingHyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or ...
Mingle Zhang +7 more
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