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Hyperspectral image classification using multiobjective optimization

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Abstract

Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, U.P., India

    Simranjit Singh, Mohit Sajwan & Deepak Garg

  2. Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhattisgarh, India

    Deepak Singh

  3. Department of Computer Science & Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

    Vijaypal Singh Rathor

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  1. Simranjit Singh
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  2. Deepak Singh
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  3. Mohit Sajwan
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  4. Vijaypal Singh Rathor
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Correspondence to Simranjit Singh.

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Singh, S., Singh, D., Sajwan, M. et al. Hyperspectral image classification using multiobjective optimization. Multimed Tools Appl 81, 25345–25362 (2022). https://doi.org/10.1007/s11042-022-12462-6

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  • DOI: https://doi.org/10.1007/s11042-022-12462-6

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