Results 61 to 70 of about 187 (133)

Combinatorial Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing Using a General $\ell _{q}$ Norm Regularization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing (HU), an essential procedure for various environmental applications, has garnered significant attention within remote sensing communities. Among different groups of HU methods, nonnegative matrix factorization (NMF)-based ones have
Saeid Gholinejad, Alireza Amiri-Simkooei
doaj   +1 more source

A Theory-Guided Transformer for Interpretable Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability.
Hongyue Cao, Fanlei Meng, Haixin Sun, Xinyu Cui, Dan Shao   +4 more
doaj   +1 more source

Hyperspectral Unmixing Using Reweighted Unidirectional TV Low-Rank NTF With Multiple-Factor Collaboration Regularization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The purpose of hyperspectral unmixing (HU) is to extract the spectral signatures and their proportion fractions from the hyperspectral remote sensing image (HSIs), which is a crucial issue in HSIs processing.
Kewen Qu, Zhenqing Li, Xiaojuan Luo, Wenxing Bao, Fangzhou Luo   +4 more
doaj   +1 more source

Spatial-spectral collaborative attention network for hyperspectral unmixing

Geocarto International
In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). Building on this, our paper aims to fully exploit the potential of the attention in transformers and apply it
Xiaojie Chen, Fanlei Meng, Ye Mo, Haixin Sun   +3 more
doaj   +1 more source

87th Annual Meeting of the Meteoritical Society 2025: Abstracts

Meteoritics &Planetary Science, Volume 60, Issue S1, Page 30-350, August 2025.
wiley   +1 more source

Efficient Progressive Mamba Model for Hyperspectral Sequence Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning-based hyperspectral unmixing has increasingly incorporated spatial information to improve performance. However, the extent of spatial information introduced involves a complex tradeoff: too little offers limited gains ...
Yang Liu, Shujun Liu, Huajun Wang
doaj   +1 more source

Hyperspectral Unmixing Using Frequency-Adaptive Convolutional-Mamba Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning (DL) has achieved remarkable progress in hyperspectral unmixing (HU) owing to its powerful feature extraction and modeling capabilities.
Zhuoyi Zhao, Xiang Xu, Chuiyi Deng, Yanyin Guo, Junwei Li, Antonio Plaza   +5 more
doaj   +1 more source

A Two-Stage Decoupling Framework for Blind Hyperspectral Unmixing: Separately Refining Endmembers and Abundances

Remote Sensing
Hyperspectral unmixing (HU) aims to estimate endmembers and their corresponding abundances, a task commonly referred to as blind hyperspectral unmixing (BLU). Nonnegative matrix factorization (NMF) provides a unified framework for their joint estimation.
Hengnuo Liu, Yulin Zhang, Yanyan Li, Yongli Wang, Xiuchuan Chen   +4 more
doaj   +1 more source

A Fast Sparse NMF Optimization Algorithm for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral remote sensing images have received extensive attention because of their high spectral resolution. However, the limitation of spatial resolution of imaging spectrometers results in a large number of mixed pixels, which restricts the ...
Kewen Qu, Zhenqing Li
doaj   +1 more source

In situ detection of water on the Moon by the Chang'E-5 lander. [PDF]

Sci Adv, 2022
Lin H, Li S, Xu R, Liu Y, Wu X, Yang W, Wei Y, Lin Y, He Z, Hui H, He H, Hu S, Zhang C, Li C, Lv G, Yuan L, Zou Y, Wang C.   +17 more
europepmc   +1 more source