Results 81 to 90 of about 265 (159)

GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness

, 2022
In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture.
Ramanayake, Lakshitha, Ekanayake, Parakrama, Jayasundara, Dhananjaya, Weerasooriya, Kavinga, Senarath, Neranjan, Wickramasinghe, Dulantha, Godaliyadda, Roshan, Herath, Vijitha, Ranasinghe, Yasiru   +8 more
core  

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

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

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

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

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

A multi-domain dual-stream network for hyperspectral unmixing

Hyperspectral unmixing is of vital importance within the realm of hyperspectral analysis, which is aimed to decide the fractional proportion (abundances) of fundamental spectral signatures (endmembers) at a subpixel level.
Tianhao Wang, Jiwei Hu, Quan Liu, Qiwen Jin, Chengli Peng   +4 more
core   +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

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