Results 71 to 80 of about 1,511 (182)

Bioanalytical TERS and Advanced Data Processing Methods

Journal of Raman Spectroscopy, Volume 57, Issue 6, Page 977-994, June 2026.
Tip‐enhanced Raman spectroscopy delivers label‐free, subnanometer vibrational imaging of biological systems. Recent advances in instrumentation, data analysis, and ambient/liquid operation enable surface‐selective mapping of molecular heterogeneity in proteins, nucleic acids, membranes, and viruses, positioning TERS as a powerful platform for nanoscale
Sarika Joshi, Ankit Dodla, Sarthak Saxena, Twinkle Soni, Sumit Saxena, Shobha Shukla, Bayden R. Wood   +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

Demonstration, validation, and application of hyperspectral microscopy for the collection of cyanobacterial spectral signatures

Limnology and Oceanography: Methods, Volume 24, Issue 6, June 2026.
Abstract Cyanobacterial and other algal blooms are an environmental concern in waterbodies worldwide. While these blooms are a nuisance for recreational activities, they can also be harmful to human and wildlife health when the algae produce and release toxins.
Natalie C. Hall, Adam C. Mumford, Aaron M. Goldfain, David W. Allen, E. Terrence Slonecker, Alisa Shtabnoy, Carl J. Legleiter, Sarah A. Spaulding   +7 more
wiley   +1 more source

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

Hyperspectral Unmixing With Multi-Scale Convolution Attention Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance.
Sheng Hu, Huali Li
doaj   +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

Tissue Classification of Breast Cancer by Hyperspectral Unmixing. [PDF]

Cancers (Basel), 2023
Jong LS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B.   +6 more
europepmc   +1 more source

Hybrid Hyperspectral Unmixing Using Fusion Mamba and Performer Attention

IEEE Access
Hyperspectral unmixing is emerging as a cutting-edge technology with applications across various areas of remote sensing. Several deep learning models, including transformer-based models, have been developed to improve unmixing accuracy.
M. Sreejam, Agilandeeswari Loganathan
doaj   +1 more source

Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing. [PDF]

Sensors (Basel), 2022
Jia X, Guo B.
europepmc   +1 more source