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access icon free Hyperspectral band selection using crossover-based gravitational search algorithm

  • Author(s): Aizhu Zhang 1, 2 ; Ping Ma 1, 2 ; Sihan Liu 3 ; Genyun Sun 1, 2 ; Hui Huang 1, 2 ; Jaime Zabalza 4 ; Zhenjie Wang 1, 2 ; Chengyan Lin 1, 2
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  • Source: Volume 13, Issue 2, 07 February 2019, p. 280 – 286
    DOI:  10.1049/iet-ipr.2018.5362 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 03/02/2018, Accepted 25/06/2018, Revised 24/04/2018, Published 17/07/2018

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.

Inspec keywords: image classification; hyperspectral imaging; particle swarm optimisation; transfer functions; search problems; data reduction

Other keywords: band dimensionality; hybrid PSO; GSA variants; band subset; novel hyperspectral band selection method; Pavia University dataset; informative subset band; combined optimisation criterion; transfer function-based CGSA; data dimensionality reduction tool; PSOGSA; crossover-based gravitational search algorithm; V-shaped transfer function; best fitness value; HSIs; classification accuracy; public hyperspectral datasets; Indian Pines dataset; hyperspectral images

Subjects: Image recognition; Computer vision and image processing techniques; Combinatorial mathematics; Optimisation techniques; Combinatorial mathematics; Optimisation techniques

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