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Orthogonal array design based multi-objective CBO and SOS algorithms for band reduction in hyperspectral image analysis

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Abstract

A hyperspectral image is taken by infrared imaging spectrometer consist of a continuous series of hundreds of bands. These bands collect spectral information across the electromagnetic spectrum. The presence of high spectral correlation among the bands have necessitated the use of dimensionality reduction in Hyperspectral image. Thus the bands which posses significant information needs to be selected and remaining noisy, correlated ones needs are rejected. In this paper the band selection task is formulated as a multi-objective optimization problem and two objective functions : Entropy and Pearson correlation coefficient are used in the analysis. The Orthogonal Array Design (OAD) is a mathematical procedure to determine few selected combinations which are effective among the total number of possible combination between vectors. It has been suitably applied in single-objective evolutionary algorithms to enhance their exploration capabilities. In this paper the OAD is hybridized with two nature inspired algorithms : Symbiotic Organisms Search (SOS) and Colliding Bodies Optimization (CBO). Resulting two algorithms termed as OAD-MOSOS and OAD-MOCBO have been applied to solve unconstrained and constrained multi-objective optimization problems. The adaptive penalty function is embodied with OAD-MOSOS and OAD-MOCBO to handle the constrained problem. Simulation results on eight benchmark functions reveal that OAD-MOSOS is accurate whereas OAD-MOCBO is computationally efficient. Both the developed algorithms are employed for band reduction in two hyperspectral images of Pavia University and Pavia Center. Simulation results reveal that in both hyperspectral images the number of retained bands using OAD-MOSOS algorithm is lowest and the clustering accuracy achieved is highest among the comparative algorithms based on MOSOS, MOCBO and NSGA-II.

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  1. Centre for Data Sciences, Institute of Technical Education and Research, Sikha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India

    Arnapurna Panda

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  1. Arnapurna Panda
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Panda, A. Orthogonal array design based multi-objective CBO and SOS algorithms for band reduction in hyperspectral image analysis. Multimed Tools Appl 82, 35301–35327 (2023). https://doi.org/10.1007/s11042-023-14510-1

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