A novel approach for multispectral satellite image classification based on the bat algorithm

Senthilnath, J., Kulkarni, Sushant, Benediktsson, J. A. and Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556 (2016) A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geoscience and Remote Sensing Letters, 13 (4). pp. 599-603. ISSN 1545-598X

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Abstract

Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems.

Item Type: Article
Additional Information: Date of Publication: 07 March 2016
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19322
Notes on copyright: Full text: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Xin-She Yang
Date Deposited: 18 Apr 2016 10:37
Last Modified: 13 Jun 2019 10:56
URI: https://eprints.mdx.ac.uk/id/eprint/19322

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