Abstract
This work presents hierarchical clustering algorithms for solving the task of crop classification using a multispectral satellite image. The hierarchical clustering algorithms uses splitting and merging techniques, where splitting is used to obtain ideal possible clusters along with its centers. The clusters with its centers are then merged based on a parametric method. Three hierarchical clustering algorithms, namely, the Niche Hierarchical Artificial Immune System (NHAIS), Niche Particle Swarm optimization (NPSO), and Niche Genetic Algorithm (NGA) are applied here for classification. To demonstrate the robustness of the proposed algorithm, results are presented for a real-time multispectral satellite image and an additional benchmark data set from the University of California, Irvine (UCI) repository. A performance comparison between all the three hierarchical clustering algorithms are presented and analyzed. The obtained results show that the NHAIS is most efficient among presented approaches.
Original language | English |
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Title of host publication | Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 |
Editors | Suresh Sundaram |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1664-1669 |
Number of pages | 6 |
ISBN (Electronic) | 9781538692769 |
DOIs | |
Publication status | Published - 28 Jan 2019 |
Event | 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: 18 Nov 2018 → 21 Nov 2018 |
Publication series
Name | Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 |
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Conference
Conference | 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 |
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Country/Territory | India |
City | Bangalore |
Period | 18/11/18 → 21/11/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Other keywords
- hierarchical clustering
- multispectral
- niche genetic algorithm
- niche hierarchical immune system
- niche particle swarm optimization