Results 51 to 60 of about 1,407 (163)

Gradient Type Methods for Linear Hyperspectral Unmixing

CSIAM Transactions on Applied Mathematics, 2022
Summary: Hyperspectral unmixing (HU) plays an important role in terrain classification, agricultural monitoring, mineral recognition and quantification, and military surveillance. The existing model of the linear HU requires the observed vector to be a linear combination of the vertices.
Xu, Fangfang, Wang, Yating, Li, Yanyan, Liu, Lu, Tian, Tonghua   +4 more
openaire   +3 more sources

Shortwave Infrared Microimaging Spectroscopy of the Martian Meteorites

Journal of Geophysical Research: Planets, Volume 131, Issue 3, March 2026.
Abstract Until samples from the Martian surface are successfully brought to Earth, meteorites represent the only opportunity to perform laboratory analyses on Martian material. Microimaging spectroscopy of the Martian meteorite suite provides a valuable means to better understand infrared data collected remotely from the Martian surface. This rapid and
J. K. Miura, B. L. Ehlmann, R. Greenberger, E. Cutts   +3 more
wiley   +1 more source

Mineralogical Classification of CRISM Hyperspectral Data Under Uncertainty With Hybrid Neural Networks

Journal of Geophysical Research: Planets, Volume 131, Issue 3, March 2026.
Abstract Orbital remote sensing observations are a lynchpin of planetary science research. Hyperspectral infrared spectroscopy in particular is key for planetary mineralogical exploration, for example, CRISM for Mars, as this underpins our understanding of the distribution of specific lithologies and the geological process leading to their formation ...
Robert Platt, Rossella Arcucci, Cédric M. John   +2 more
wiley   +1 more source

Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review

Remote Sensing
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has ...
Jinlin Zou, Hongwei Qu, Peng Zhang
doaj   +1 more source

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Remote Sensing, 2019
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj   +1 more source

Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms

Canadian Journal of Remote Sensing, 2020
Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed ...
Dongmei Song, Ning Sun, Mingming Xu, Bin Wang, Ling Zhang   +4 more
doaj   +1 more source

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

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Deep Spectral Convolution Network for Hyperspectral Unmixing [PDF]

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures ...
Ozkan, Savas, Akar, Gozde Bozdagi
openaire   +2 more sources

AVIRIS‐3 Rapid Response to January 2025 Los Angeles Wildfires

Geophysical Research Letters, Volume 53, Issue 2, 28 January 2026.
Abstract Wildfires in wildland‐urban interfaces (WUIs) are a growing concern due to their devastating impact on human communities and ecosystems. Low‐latency impact assessment is critical for wildfire response, yet immediate access to fire‐affected communities can be limited.
Megan Ward‐Baranyay, Red Willow Coleman, Mark Wronkiewicz, Francisco Ochoa, Daniel Sousa, Ceth W. Parker, David R. Thompson, Clayton D. Elder, Gregory S. Okin, K. Dana Chadwick, Kimberley Miner, John Chapman, Regina Eckert, Daniel Jensen, Robert O. Green, Philip G. Brodrick   +15 more
wiley   +1 more source