Senthilnath, J and Kulkarni, Sushant and Benediktsson, JA and Yang, XS (2016) A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm. In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 13 (4). pp. 599-603.
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Among the multiple advantages and applications of remote sensing, one of the most important uses 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 letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral 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 multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.
Item Type: | Journal Article |
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Publication: | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Publisher: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
Additional Information: | Copy right for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA |
Keywords: | Bat algorithm (BA); clustering; genetic algorithm (GA); multispectral satellite image; particle swarm optimization (PSO) |
Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
Date Deposited: | 28 Apr 2016 06:04 |
Last Modified: | 28 Apr 2016 06:04 |
URI: | http://eprints.iisc.ac.in/id/eprint/53712 |
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