Identificador persistente para citar o vincular este elemento: https://accedacris.ulpgc.es/handle/10553/142179
Título: Unified Unsupervised Unmixing with Sparse Noise Estimation for Linear and Multilinear Models
Autores/as: Campos Delgado, Daniel Ulises 
Mendoza-Chavarría, Juan Nicolás
Gutierrez-Navarro, Omar
Quintana Quintana, Laura 
Callicó, Gustavo 
Clasificación UNESCO: 33 Ciencias tecnológicas
Palabras clave: Correlated Multimodal Images
Linear Unmixing
Multilinear Unmixing
Sparse Noise
Fecha de publicación: 2025
Publicación seriada: IEEE Signal Processing Letters 
Resumen: Multimodal images (MIs) can capture different modalities of a scene with multiple applications in medicine, remote sensing, food inspection, among others. Over a 2D domain, these images acquire spectral/morphological/temporal information of each spatial point. Unmixing methodologies can decompose this spatial and spectral/morphological/temporal information. In this letter, a unified framework is proposed for unsupervised unmixing, explicitly accounting for Gaussian and sparse noise. Our approach is novel in three key aspects: (i) addresses the general case of multimodal images, (ii) unifies linear and multilinear mixing models, and (iii) incorporates noise effects into the synthesis schemes. The proposed methodology relies on cyclic coordinate descent optimization (CCDO), constrained quadratic estimation, and L1-regularization. For the validation stage, two types of synthetic MIs were used with additive Gaussian and sparse noise terms. Additionally, the Urban dataset was employed for further validation to consider a real-world scenario. The results show that the proposed methodologies provide accurate reconstructions of the datasets, as well as the ground-truth abundance maps and end-members with low computational time.
URI: https://accedacris.ulpgc.es/handle/10553/142179
ISSN: 1070-9908
DOI: 10.1109/LSP.2025.3582539
Fuente: IEEE Signal Processing Letters[ISSN 1070-9908], (Enero 2025)
Colección:Artículos
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