Symbolic agglomerative clustering for quantitative analysis of remotely sensed data

$63.37 plus tax (Refund Policy)

Buy Article:


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: November 20, 2000

More about this publication?
Related content

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more