Results 91 to 100 of about 919,951 (170)

Endmember Independence and Bilateral Filtering Regularizations for Blind Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral 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
doaj   +1 more source

A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing

Remote Sensing
Hyperspectral 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, Mingyu Yang, Hongyu Xie, Pinliang Yue, Wei Zhang, Qingbin Jiao, Liang Xu, Xin Tan   +7 more
doaj   +1 more source

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
doaj   +1 more source

Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

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, Kang Zhao, Congying Han, Ziwen Liu   +3 more
doaj   +1 more source

SSTNT: A Spatial–Spectral Similarity Guided Transformer-in-Transformer for Hyperspectral Unmixing

Photonics
Vision Transformers (ViTs), owing to their strong capability in modeling global contextual dependencies, have been widely adopted in hyperspectral image unmixing (HU).
Xinyu Cui, Xinyue Zhang, Aoran Dai, Da Sun   +3 more
doaj   +1 more source

AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing

, 2022
International audienceDue to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU).
Hong, Danfeng, Zhang, Bing, Gao, Lianru, Chanussot, Jocelyn, Han, Zhu, Huang, Min, Danfeng Hong, Jocelyn Chanussot, Zhu Han, Lianru Gao, Min Huang, Bing Zhang   +11 more
core   +1 more source

Multimodal Hyperspectral Unmixing: Insights From Attention Networks

, 2022
International audienceDeep learning (DL) has aroused wide attention in hyperspectral unmixing (HU) owing to its powerful feature representation ability.
Hong, Danfeng, Zhang, Bing, Gao, Lianru, Yao, Jing, Chanussot, Jocelyn, Han, Zhu, Danfeng Hong, Jocelyn Chanussot, Zhu Han, Lianru Gao, Jing Yao, Bing Zhang   +11 more
core   +1 more source

88th Annual Meeting of the Meteoritical Society 2026: Abstracts

Meteoritics &Planetary Science, Volume 61, Issue S1, Page S48-S551, August 2026.
wiley   +1 more source

Abstracts

Molecular Oncology, Volume 19, Issue S1, Page 1-940, June 2025.
Abstracts submitted to the ‘EACR 2025 Congress: Innovative Cancer Science’, from 16–19 June 2025 and accepted by the Congress Organising Committee are published in this Supplement of Molecular Oncology, an affiliated journal of the European Association for Cancer Research (EACR).
wiley   +1 more source

Adaptive Multiorder Graph Regularized NMF With Dual Sparsity for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order or second-order nearest neighbor relationships and usually ...
Hui Chen, Liangyu Liu, Xianchao Xiu, Wanquan Liu   +3 more
doaj   +1 more source