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High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 54 - Number 1
Year of Publication: 2012
Authors:
C. Pandimuthu
K. Kuppusamy
10.5120/8533-2065

C Pandimuthu and K Kuppusamy. Article: High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm. International Journal of Computer Applications 54(1):31-35, September 2012. Full text available. BibTeX

@article{key:article,
	author = {C. Pandimuthu and K. Kuppusamy},
	title = {Article: High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {54},
	number = {1},
	pages = {31-35},
	month = {September},
	note = {Full text available}
}

Abstract

The high-resolution commercial satellite imagery (HRCSI) has increased significantly over the last 5 years for a wide variety of applications. This has increase in volume, frequency of acquisition, and spatial resolution of HRCSI. In particular, satellite images contain land cover types; large areas (e. g. , building, bridge and roads) occupy relatively small regions. The change detection and exploitation of change between multi temporal high-resolution satellite and air bone images. Overlapping multi temporal images are first organized in to 256m x 256m tiles in a global grid reference system. The tiles are initially ranged by these changes scores for retrieval, review, and exploitation in web based applications. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper we present an agglomerative fuzzy K-Means clustering algorithm in change detection. The algorithm can produce more consistent clustering result from different sets of initial clusters centres, the algorithm determine the number of clusters in the data sets, which is a well – known problem in K-means clustering.

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