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Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

Senthilnath, J and Omkar, SN and Mani, V and Tejovanth, N and Diwakar, PG and Shenoy, Archana B (2012) Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images. In: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 5 (3, SI). pp. 762-768.

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Official URL: http://dx.doi.org/10.1109/JSTARS.2012.2187432

Abstract

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to the IEEE
Keywords: Glowworm swarm optimization;mean shift clustering; niche particle swarm optimization
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering (Formerly, Aeronautical Engineering)
Date Deposited: 31 Jul 2012 12:03
Last Modified: 31 Jul 2012 12:03
URI: http://eprints.iisc.ernet.in/id/eprint/44900

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