Hyperspectral band selection using crossover‐based gravitational search algorithm. Issue 2 (1st February 2019)

• Record Type: Journal Article
• Authors: Zhang, Aizhu; Ma, Ping; Liu, Sihan; Sun, Genyun; Huang, Hui; Zabalza, Jaime; Wang, Zhenjie; Lin, Chengyan
• Is Part Of: IET image processing. Volume 13:Issue 2(2019)
• Page Start: 280
• Page End: 286
• Publication Date: 2019-02-01
• Subjects: search problems -- image classification -- particle swarm optimisation -- hyperspectral imaging -- data reduction -- transfer functions; crossover‐based gravitational search algorithm -- hyperspectral images -- HSIs -- informative subset band -- novel hyperspectral band selection method -- band subset -- transfer function‐based CGSA -- public hyperspectral datasets -- band dimensionality -- data dimensionality reduction tool -- combined optimisation criterion -- classification accuracy -- V‐shaped transfer function -- best fitness value -- Indian Pines dataset -- Pavia University dataset -- PSOGSA -- hybrid PSO -- GSA variants; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5362
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover‐based gravitational search algorithm (CGSA) is presented in this study. In this method, the discriminative capability of each band subset is evaluated by a combined optimisation criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V ‐shaped transfer function‐based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants.