Results 81 to 90 of about 77,969 (223)

DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in
S. Khoshsokhan, R. Rajabi, H. Zayyani
doaj   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, Volume 38, Issue 51, 11 September 2026.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Multiple graph regularized NMF for hyperspectral unmixing

, 2015
Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into ...
Lei Tong, Jun Zhou, Zhou, Jun, Qian, Yuntao, Yuntao Qian, Tong, Lei, Yongsheng Gao, Gao, Yongsheng   +7 more
core   +1 more source

Phasor-Based Multi-Harmonic Unmixing for in-vivo Hyperspectral Imaging

, 2022
Included are the MATLAB scripts for spectral phasor multiharmonic unmixing and general image processing to genereate the simulations and figures in the paper entitled 'Phasor-Based Multi-Harmonic Unmixing for in-vivo Hyperspectral Imaging'
Alexander Vallmitjana (9636680)
core   +1 more source

Deep multimodal unmixing of hyperspectral images using Convolutional Block Attention Module (CBAM) and LiDAR features

Egyptian Journal of Remote Sensing and Space Sciences
Hyperspectral image unmixing has garnered considerable attention across various application domains, particularly remote sensing applications. However, relying solely on one modality to distinguish objects with similar spectral information presents ...
M Sreejam, L Agilandeeswari
doaj   +1 more source

Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2014
Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +2 more sources

Identification of an Osteochondral Erosion Process in Gout Driven by Fibrinogen‐Integrin Activation and Local MSU Crystallization

Advanced Science, Volume 13, Issue 54, 28 September 2026.
Cartilage injury promotes local fibrinogen deposition, which accelerates monosodium urate crystallization and activates integrin‐mediated matrix‐degradation. This self‐amplifying cycle drives gout‐related cartilage erosion. Disrupting fibrinogen deposition or restoring the cartilage barrier could interrupt this vicious cycle.
Hanlin Xu, Simin Bi, Zhechen Liu, Xiaofeng Zhou, Hengwei Qin, Nicola Dalbeth, Minbiao Ji, Yinghui Hua   +7 more
wiley   +1 more source

Projection-Based NMF for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
As a widely concerned research topic, many advanced algorithms have been proposed for hyperspectral unmixing. However, they may fail to accurately identify endmember signatures when coming across insufficient spatial resolution. To deal with this problem, an algorithm based on semisupervised linear sparse regression is proposed, in which unmixing ...
Yuan, Yuan, Feng, Yachuang, Lu, Xiaoqiang   +2 more
openaire   +3 more sources

Early Detection of Drought and Overwatering Stress in Apple Seedlings Using Hyperspectral Imaging and Deep Learning

Food and Energy Security, Volume 15, Issue 5, September/October 2026.
This study presents a robust framework for early water stress diagnosis in apple seedlings using hyperspectral imaging and a dual‐attention SimAM‐ECA‐MobileNet model. By integrating endmember extraction and 2D correlation analysis, the method achieves 93.33% accuracy, significantly outperforming baseline models for non‐invasive health monitoring in ...
Yanying An, Yunxiang Zang, Wei Zeng, Yunlong Zhang, Zhengyan Ling, Ali Zia, Jie Liu, Yuan Zhan, Zhongzhi Han, Ran Wang, Hao Liang, Kim Johnson   +11 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