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 |
Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.