Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification

• Published in: Multimedia tools and applications 2024-03, Vol.83 (9), p.26901-26927
• Main Authors: Giri, Ram Nivas, Janghel, Rekh Ram, Pandey, Saroj Kumar
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Convergence; Data Structures and Information Theory; Exploitation; Heuristic methods; Hybridization; Hyperspectral imaging; Image classification; Iterative methods; Multimedia Information Systems; Particle swarm optimization; Redundancy; Search algorithms; Search process; Special Purpose and Application-Based Systems; Spectral bands
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-16638-6

A Hyperspectral image (HSI) contains numerous spectral bands, providing better differentiation of ground objects. Although the data from HSI are very rich in information, their processing presents some difficulties in terms of computational effort and reduction of information redundancy. These difficulties stem mainly from the fact that the HSI consists of a large number of bands along with some redundant bands. Band selection (BS) is used to select a subset of bands to reduce processing costs and eliminate spectral redundancy. BS methods based on a metaheuristic approach have become popular in recent years. However, most BS methods based on a metaheuristic approach can get stuck in the local optimum and converge slowly due to a lack of balance between exploration and exploitation. In this paper, three BS methods are proposed for HSI data. The first method applies Crow Search Algorithm (CSA) for BS. The other two proposed methods, HPSOCSA_SP and HPSOCSA_SLP, are based on the hybridization of Particle Swarm Optimization (PSO) and CSA. The purpose of these hybridizations is to balance exploration and exploitation in a search process for optimal band selection and fast convergence. In hybridization techniques, PSO and CSA exchange informative data at each iteration. HPSOCSA_SP split the population into two equal parts. PSO is applied to one part and CSA to the other. HPSOCSA_SLP selects half of the top-performing members based on fitness. PSO and CSA are applied to the selected population sequentially. Our proposed models underwent rigorous testing on four HSI datasets and showed superiority over other metaheuristic techniques.