Results 91 to 100 of about 9,243 (189)

Hyperspectral EELS image unmixing

, 2016
Electron Energy Loss Spectroscopy (EELS) performed in a Scanning Transmission Electron Microscope (STEM) provides hyperspectral images characterized by a large number of pixels and energy channels (typically 100 x 100 x 1000) [1].
Altmann, Yoann, Brun, Nathalie, Dobigeon, Nicolas, March, Katia, Moussaoui, Saïd, Schneegans, Olivier   +5 more
openaire   +2 more sources

CCTV‐Hyperspectral Imaging for Suspended Sediment Transport (HISST): Proof‐of‐Concept for a Continuous Day‐and‐Night Monitoring Approach

Water Resources Research, Volume 61, Issue 10, October 2025.
Abstract Effective sediment monitoring is crucial for managing dynamic river environments where suspended sediment transport varies over time. However, manual sampling and turbidity sensor‐based methods provide limited spatial coverage and can be labor‐intensive.
Siyoon Kwon, Hyoseob Noh, Il Won Seo, Yun Ho Lee   +3 more
wiley   +1 more source

TCCU-Net: Transformer and CNN Collaborative Unmixing Network for Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep-learning-based hyperspectral unmixing techniques have garnered increasing attention and made significant advancements. However, relying solely on the use of convolutional neural network (CNN) or transformer approaches is ...
Jianfeng Chen, Chen Yang, Lan Zhang, Linzi Yang, Lifeng Bian, Zijiang Luo, Jihong Wang   +6 more
doaj   +1 more source

Exploring a Hybrid Convolutional Framework for Camouflage Target Classification in Land‐Based Hyperspectral Images

CAAI Transactions on Intelligence Technology, Volume 10, Issue 5, Page 1559-1572, October 2025.
ABSTRACT In recent years, camouflage technology has evolved from single‐spectral‐band applications to multifunctional and multispectral implementations. Hyperspectral imaging has emerged as a powerful technique for target identification due to its capacity to capture both spectral and spatial information.
Jiale Zhao, Dan Fang, Jianghu Deng, Jiaju Ying, Yudan Chen, Guanglong Wang, Bing Zhou   +6 more
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

Characterization of a 0.5 × 0.5 mm plastic scintillating detector for small field dosimetry: Its performance and comparison with other commercially available plastic scintillating detectors

Journal of Applied Clinical Medical Physics, Volume 26, Issue 9, September 2025.
Abstract Background Lack of an ideal detector for small field dosimetry has led to the development of many new types of detectors. Recent studies have shown that plastic scintillation detectors (PSDs) provide favorable dosimetric characteristics, such as minimal volume averaging and fluence perturbation effects, real time response rates, high signal to
Milad Baradaran‐Ghahfarokhi, Beshoi Grees, Annie Cooney, Erica Northern, George Ding, Guozhen Luo, Kenneth Homann   +6 more
wiley   +1 more source

Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing ...
Li Wang, Xiaohua Zhang, Jinhua Zhang, Hua Dong, Hongyun Meng, Licheng Jiao   +5 more
doaj   +1 more source

Dictionary-based Tensor Canonical Polyadic Decomposition

, 2017
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary.
Cohen, Jérémy E., Gillis, Nicolas
core   +1 more source

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition.
Chong Zhao, Jinlin Wang, Qingqing Qiao, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Dong Li, Tao Liao, Chao Li, Heshun Qiu, Guangjun Qu   +11 more
doaj   +1 more source

Mapping Peatlands Combing Deep Learning With Sparse Spectral Unmixing Based on Zhuhai-1 Hyperspectral Images

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