Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging

EasyChair Preprint 9883

Abstract

An approach to parameter optimization for the low-rank matrix recovery method (LRMR) in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the parameters of LRMR. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as quantitative measure. The optimization method is tested on standard and openly available hyperspectral data sets including Indian Pines.

Keyphrases: Computational time, Hyperspectral imaging, Noise Ratio, Optimization, data set, indian pine data, low rank, low-rank matrix recovery, low-rank modeling, noise reduction, remote sensing, signal-to-noise ratio improvement

@booklet{EasyChair:9883,
  author    = {Monika Wolfmayr},
  title     = {Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging},
  howpublished = {EasyChair Preprint 9883},
  year      = {EasyChair, 2023}}