Simulated Annealing-Based Optimization for Band Selection in Hyperspectral Image Classification

Authors

  • Said Khelifa University of Science and Technology of Oran Mohamed Boudiaf
  • Fatima Boukhatem University of Sidi Bel Abbes Djillali Liabes
  • Leila Benaissa Kaddar University of Mustapha Stambouli Mascara

DOI:

https://doi.org/10.13053/cys-27-4-4519

Keywords:

Optimization, band selection, classification, bagging, correlation, simulated annealing

Abstract

In this paper, a new optimizationbased framework for hyperspectral image classificationproblem is proposed. Band selection is a primordialstep in supervised/unsupervised hyperspectral imageclassification. It attempts to select an optimal subset ofspectral bands from the entire set of hyperspectral cube.This subset is considered as the relevant informativesubset of bands. The advantage of an efficient bandselection approach is to reduce the hughes phenomenonby removing irrelevant and redundant bands. In thisstudy, we propose a new objective function for theband selection problem by using Simulated Annealingas an optimization method. The proposed approach istested on three Hyperspectral Images largely used in theliterature. Experimental results show the performanceand efficiency of the proposed approach.

Author Biography

Said Khelifa, University of Science and Technology of Oran Mohamed Boudiaf

SIMPA Laboratory, Computer Science Department

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Published

2023-12-17

Issue

Vol. 27 No. 4 (2023): 27(4) 2023

Section

Articles

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