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Communication Dans Un Congrès Année : 2020

A semi-supervised rank tracking algorithm for on-line unmixing of hyperspectral images

Résumé

This paper addresses the problem of rank tracking in real time hyperspectral image unmixing. Based on the On-line Alternating Direction Method of Multipliers (ADMM), we propose a new hyperspectral unmixing approach that integrates prior information as well as joint sparsity regularization, allowing to select only the active components on each sample of the image. This results in a semi-supervised algorithm, well adapted for on-line rank tracking for pushbroom imager. Experimental results on synthetic and real data sets demonstrate the effectiveness of our method for parameter estimation and rank change detection.
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Dates et versions

hal-02477639 , version 1 (13-02-2020)

Identifiants

Citer

Ludivine Nus, Sebastian Miron, Benoît Jaillais, Said Moussaoui, David Brie. A semi-supervised rank tracking algorithm for on-line unmixing of hyperspectral images. International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, May 2020, Barcelone, Spain. ⟨10.1109/ICASSP40776.2020.9053931⟩. ⟨hal-02477639⟩
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