A Semi-Supervised Rank Tracking Algorithm For On-Line Unmixing Of Hyperspectral Images

Ludivine Nus; Sebastian Miron; Benoît Jaillais; Saïd Moussaoui; David Brie

ICASSP 2020accepted0 citations

A Semi-Supervised Rank Tracking Algorithm For On-Line Unmixing Of Hyperspectral Images

Ludivine Nus, Sebastian Miron, Benoît Jaillais, Saïd Moussaoui, David Brie

Abstract

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.

BibTeX

@inproceedings{icassp2020_asemisupervisedr,
  title = {A Semi-Supervised Rank Tracking Algorithm For On-Line Unmixing Of Hyperspectral Images},
  author = {Ludivine Nus and Sebastian Miron and Benoît Jaillais and Saïd Moussaoui and David Brie},
  booktitle = {ICASSP 2020},
  year = {2020}
}