Results 61 to 70 of about 1,511 (182)

Spatial Structural Priors for Sparse Unmixing of Remotely Sensed Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
As spectral libraries continue to expand, sparse unmixing has become essential for effectively interpreting mixed pixels in remotely sensed hyperspectral data.
Shaoquan Zhang, Jiajun Zheng, Yuyang Liu, Fan Li, Chengzhi Deng, Antonio Plaza, Lianhui Liang, Zhi He, Shengqian Wang   +8 more
doaj   +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   +2 more sources

The Magnetic Mineralogy of Carbonaceous Chondrites: A Microscopy and Machine Learning Study of Meteorite WIS 91600

Geochemistry, Geophysics, Geosystems, Volume 27, Issue 7, July 2026.
Abstract A central challenge in the paleomagnetic study of meteorites is to characterize their diverse remanence carriers. Here, we develop a machine‐learning assisted workflow that combines multi‐scale (mm to nm) and multi‐dimensional (2D‐3D) microscopy to build a comprehensive picture of ferromagnetic mineralogy in the C2 ungrouped carbonaceous ...
Richard J. Harrison, Po‐Yen Tung, Thomas Ginnis, Farhang Nabiei, Ursula Shaw, Elias N. Mansbach, James Bryson   +6 more
wiley   +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

Remote Sensing for Monitoring and Managing Urban Soils in Coastal Areas: A Review

Soil Use and Management, Volume 42, Issue 3, July‐September 2026.
ABSTRACT Coastal urban soils (CUS) exhibit distinct physical–chemical properties due to the combined effects of intense interaction among anthropogenic and natural pressures, resulting in a unique, complex and highly vulnerable system. While CUS are vital for the urban coastal environment and its inhabitants, numerous factors significantly threaten ...
Antonio Ganga, Alessandro Auzzas, Rafael Barroca Silva, Gian Franco Capra   +3 more
wiley   +1 more source

Integration of Raman Spectroscopy and Metabolomics for Early Breast Cancer Detection and Classification

Cancer Medicine, Volume 15, Issue 6, June 2026.
ABSTRACT Breast cancer, now the fourth leading cause of cancer‐related mortality worldwide, necessitates early detection for improved clinical outcomes. Conventional histopathology, though widely used, is invasive and subjective, limiting its utility in early‐stage diagnosis.
Xue Li, He Ren, Yuhan Deng, Yang Li, Fangjie Hu   +4 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

Conceptual Differences Between Hyperspectral Unmixing, PCA, and Clustering: A Practical Demonstration Using Single‐Cell Raman Imaging

Journal of Chemometrics, Volume 40, Issue 6, June 2026.
ABSTRACT Hyperspectral unmixing is a well‐established approach for analyzing spectroscopic images by recovering chemically interpretable component spectra and their spatial distributions. Despite its distinct analytical objective, unmixing is frequently discussed alongside or directly compared with more familiar multivariate techniques such as ...
Rustam R. Guliev, Ute Neugebauer
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

A Spectral Unmixing Method by Maximum Margin Criterion and Derivative Weights to Address Spectral Variability in Hyperspectral Imagery

Remote Sensing, 2019
Limited to the low spatial resolution of the hyperspectral imaging sensor, mixed pixels are inevitable in hyperspectral images. Therefore, to obtain the endmembers and corresponding fractions in mixed pixels, hyperspectral unmixing becomes a hot spot in ...
Yang Shao, Jinhui Lan
doaj   +1 more source