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Proceedings Paper

Optimal splitting technique for remote sensing satellite imagery data
Author(s): Debasis Chaudhuri; A. Mishra; S. K. Anand; V. Gohri
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Paper Abstract

Target detection in satellite images requires multilevel processing. One of the important levels of processing is image segmentation. In this, a gray level image is segmented into some limited number of classes. One of these segmentation techniques is clustering. Cluster analysis is the formal study of algorithms and methods for grouping. Most of the K-means class of methods start with some initial seed points and grow cluster around them. There exists two basic problems related to all seed based techniques - choice of appropriate initial seed points and single seed based techniques are not effective of more complex and elongated shape. Any elongated or non-convex cluster can be considered as the union of a few distinct hyper-spherical clusters. A multi-seed clustering method based on the border points proposed by Chaudhuri and Chaudhuri but, the border point extraction from remote sensing satellite imagery (RSSI) and noisy data is a difficult job. RSSI data has three basic characteristics - non-parametric, highly-overlapping and non-Gaussian. The proposed seed-point detection technique is statistical based and can solve the non-parametric as well as non-Gaussian problems. Also it focuses on the non-Gaussian field as the union of several Gaussian fields. Each seed point has a core set. After finding the seed points, an initial splitting technique (IST) based on the min-min-metric operation of the parameters mode and mean of each core set is proposed. Since RSSI data is highly overlapping, so IST algorithm can not cluster properly in the overlapping region. Another type of splitting technique is introduced in the overlapping regions. For finding the pair of overlapping clusters and overlapping regions minimal spanning tree based and set theoretic based mathematical formulation have been developed in this paper. Finally, the optimal splitting technique in the overlapping region based on maximizing the between class variance is introduced. This algorithm is tested on several real life data.

Paper Details

Date Published: 28 August 2001
PDF: 10 pages
Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); doi: 10.1117/12.438244
Show Author Affiliations
Debasis Chaudhuri, Government of India (India)
A. Mishra, Government of India (India)
S. K. Anand, Government of India (India)
V. Gohri, Government of India (India)

Published in SPIE Proceedings Vol. 4388:
Visual Information Processing X
Stephen K. Park; Zia-ur Rahman; Robert A. Schowengerdt, Editor(s)

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