Download PDFOpen PDF in browserA Comparative Study of Recent Multi-Component Unmixing AlgorithmsEasyChair Preprint 47535 pages•Date: December 20, 2020AbstractIn this paper, we consider the problem of blind multicomponent image unmixing. Two mixing models are considered : the linear mixing model (LMM) and its extended version (ELMM) which take the spectral (i.e. endmember) variability into account. We introduce powerful unmixing algorithms utilizing these models of latest state-of-the-art, and compare their performance on endmember recovery and abundance estimation. Keyphrases: Evaluation Assessment, hyperspectral image, multispectral image, spectral unmixing
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