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Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification

  • Data analytics and machine learning
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Abstract

Dimensionality reduction (DR) is an effective preprocessing step in hyperspectral image (HSI) analysis. A particle swarm optimization (PSO)-based unsupervised DR method is proposed in the present paper where spectral divergence as well as spatial gradient information is used in selecting informative bands. In general, HSI is negatively affected by low signal-to-noise ratio (SNR). Hence, in the proposed method, a noise filter is applied to minimize the effect of noise in band selection. Clustering is introduced to reduce spatial redundancy and extract distinct patterns from the data. This enables improvement in the computation performance of each iteration in PSO. The proposed method is applied on two standard datasets, and the performance is evaluated using overall classification accuracy. Finally, results are compared with other recent state-of-the-art methods where the proposed method performed reasonably better than other tested methods in terms of consistency and classification accuracy.

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Data sharing is not applicable to this article as no new data were created or analysed in this study.

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Authors and Affiliations

  1. Regional Remote Sensing Centre-East, NRSC, ISRO, Kolkata, India

    Arati Paul

  2. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Nabendu Chaki

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  1. Arati Paul
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  2. Nabendu Chaki
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Correspondence to Arati Paul.

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Paul, A., Chaki, N. Band selection using spectral and spatial information in particle swarm optimization for hyperspectral image classification. Soft Comput 26, 2819–2834 (2022). https://doi.org/10.1007/s00500-022-06821-6

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