Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/133376
Título: Blind non-linear spectral unmixing with spatial coherence for hyper and multispectral images
Autores/as: Mendoza-Chavarría, Juan N.
Cruz-Guerrero, Inés A.
Gutierrez-Navarro, Omar
León, Raquel 
Ortega, Samuel 
Fabelo, Himar 
Callicó, Gustavo M. 
Campos Delgado, Daniel Ulises 
Clasificación UNESCO: 33 Ciencias tecnológicas
Palabras clave: Hyperspectral Imaging
Multi-Linear Model
Multispectral Imaging
Non-Linear Unmixing
Total Variation
Fecha de publicación: 2024
Publicación seriada: Journal of the Franklin Institute 
Resumen: Multi and hyperspectral images have become invaluable sources of information, revolutionizing various fields such as remote sensing, environmental monitoring, agriculture and medicine. In this expansive domain, the multi-linear mixing model (MMM) is a versatile tool to analyze spatial and spectral domains by effectively bridging the gap between linear and non-linear interactions of light and matter. This paper introduces an upgraded methodology that integrates the versatility of MMM in non-linear spectral unmixing, while leveraging spatial coherence (SC) enhancement through total variation theory to mitigate noise effects in the abundance maps. Referred to as non-linear extended blind end-member and abundance extraction with SC (NEBEAE-SC), the proposed methodology relies on constrained quadratic optimization, cyclic coordinate descent algorithm, and the split Bregman formulation. The validation of NEBEAE-SC involved rigorous testing on various hyperspectral datasets, including a synthetic image, remote sensing scenarios, and two biomedical applications. Specifically, our biomedical applications are focused on classification tasks, the first addressing hyperspectral images of in-vivo brain tissue, and the second involving multispectral images of ex-vivo human placenta. Our results demonstrate an improvement in the abundance estimation by NEBEAE-SC compared to similar algorithms in the state-of-the-art by offering a robust tool for non-linear spectral unmixing in diverse application domains.
URI: http://hdl.handle.net/10553/133376
ISSN: 0016-0032
DOI: 10.1016/j.jfranklin.2024.107282
Fuente: Journal of the Franklin Institute[ISSN 0016-0032],v. 361 (18), (Diciembre 2024)
Colección:Artículos
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