Minimum distance criterion for non-negative hyperspectral image deconvolution

Yingying Song; David Brie; El-Hadi Djermoune; Simon Henrot

ICASSP 2016accepted0 citations

Minimum distance criterion for non-negative hyperspectral image deconvolution

Yingying Song, David Brie, El-Hadi Djermoune, Simon Henrot

Abstract

This work aims at studying a method to automatically estimate regularization parameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) is proposed to estimate regularization parameters. It has good theoretical properties (uniqueness, robustness) from which a grid search based approach is proposed. It results in a fast approach to estimate the regularization parameters.

BibTeX

@inproceedings{icassp2016_minimumdistancec,
  title = {Minimum distance criterion for non-negative hyperspectral image deconvolution},
  author = {Yingying Song and David Brie and El-Hadi Djermoune and Simon Henrot},
  booktitle = {ICASSP 2016},
  year = {2016}
}