Symbolic agglomerative clustering for quantitative analysis of remotely sensed data

Srikanta Prakash, H. N. and Nagabhushan, P. and Chidananda Gowda, K. (2000) Symbolic agglomerative clustering for quantitative analysis of remotely sensed data. International Journal of Remote Sensing, 21 (17). pp. 3239-3251. ISSN 0143-1161

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Abstract

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index ( MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.

Item Type:	Article
Additional Information:	International Conference on Remote Sensing and GIS/GPS 1997 (ICORG 97), HYDERABAD, INDIA, JUN 18-21, 1997
Subjects:	D Physical Science > Computer Science
Divisions:	Department of > Computer Science
Depositing User:	Users 19 not found.
Date Deposited:	19 Sep 2019 09:51
Last Modified:	19 Sep 2019 09:51
URI:	http://eprints.uni-mysore.ac.in/id/eprint/8277

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