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Symbolic agglomerative clustering for quantitative analysis of remotely sensed data

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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.

Document Type: Research Article


Affiliations: 1: Image Processing Lab, CS & E Department, S.J. College of Engineering, Mysore 570 006, India 2: Department of Studies in Computer Science, University of Mysore, Manasagangotri, Mysore 570 006, India 3: CS & E Department, S.J. College of Engineering, Mysore 570 006, India

Publication date: 2000-11-20

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