A new hybrid SSA-TA: Salp Swarm Algorithm with threshold accepting for band selection in hyperspectral images

• Published in: Applied soft computing Vol. 95; p. 106534
• Main Authors: Medjahed, Seyyid Ahmed, Ouali, Mohammed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2020
• Subjects: Band selection; Hybrid metaheuristic; Hyperspectral image classification; Salp Swarm Algorithm; Threshold Acceptance; Threshold Acceptance; Band selection; Hybrid metaheuristic; Salp Swarm Algorithm; Hyperspectral image classification
• ISSN: 1568-4946
• DOI: 10.1016/j.asoc.2020.106534

Hyperspectral images classification is a primordial step to produce the Land Use maps. Unfortunately, the classification accuracy depends largely on the quality of spectral bands. Several bands are non-informative and the adjacent bands are generally highly correlated. This paper presents a novel band selection approach named SSA-TA based on Salp Swarm Algorithm (SSA) which a new metaheuristic recently developed and Threshold Acceptance(TA). The proposed approach SSA-TA is a hybrid metaheuristic used to select the relevant bands by eliminating the irrelevant and redundant bands to enhance the hyperspectral image classification. This work presents two main ideas. Firstly, we propose a hybridization model based on SSA and Threshold Acceptance (TA). The basic idea is using SSA to find the promising region and use TA to enhance the exploration of the best solution. Secondly, the fitness function is designed to take into consideration three important terms: (1) the maximization of classification accuracy rate (2) the minimization of the number of selected bands (3) the minimization of correlated bands. The performance evaluation of the proposed approach is tested on three hyperspectral images widely used on remote sensing. The proposed approach is compared to other algorithms. The experimental results demonstrate the efficiency of our approach in improving the classification accuracy rate. [Display omitted] •A novel band selection approach is proposed for hyperspectral image classification.•A new hybrid metaheuristic is proposed based on SSA and Threshold Acceptance.•A new objective function is designed based on three terms.•The experimentation is conducted on three images widely used in the literature.•The numerical results show the efficacy of the proposed approach compared to other.