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Hyperspectral image classification using support vector machine: a spectral spatial feature based approach

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Abstract

Recently spectral–spatial information based algorithms are gaining more attention because of its robustness, accuracy and efficiency. In this paper, an SVM based classification method has been proposed which extracts features considering both spectral and spatial information. The proposed method exploits SVM to encode spectral–spatial information of pixel and also used for classification task. A clean comparison of relative gain achieved with inclusion of spatial features with its spectral counterpart is also investigated. The experiment has been performed using three benchmark datasets Indian Pines, Pavia University and Salinas. Experiments show that the proposed method outperforms the classification algorithms K nearest neighbors, linear discriminant analysis, Naive Bayes and decision tree.

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Acknowledgements

The work is performed under the Visvesvaraya Ph.D. Fellowship Grant of Ministry of Electronics and Information Technology (Meity), India. The authors acknowledge the support of Meity for facilitating the work. The authors also acknowledge the anonymous reviewer for their valueable suggestion.

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Authors and Affiliations

  1. Department of Computer Science, Gauhati University, Guwahati, Assam, India

    Diganta Kumar Pathak & Sanjib Kumar Kalita

  2. Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, India

    Dhruba Kumar Bhattacharya

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  1. Diganta Kumar Pathak
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  2. Sanjib Kumar Kalita
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  3. Dhruba Kumar Bhattacharya
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Correspondence to Diganta Kumar Pathak.

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Pathak, D.K., Kalita, S.K. & Bhattacharya, D.K. Hyperspectral image classification using support vector machine: a spectral spatial feature based approach. Evol. Intel. 15, 1809–1823 (2022). https://doi.org/10.1007/s12065-021-00591-0

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