A Design of Clustering Classification Systems using Satellite Remote Sensing Images Based on Design Patterns


The KIPS Transactions:PartB , Vol. 9, No. 3, pp. 319-326, Jun. 2002
10.3745/KIPSTB.2002.9.3.319,   PDF Download:

Abstract

In this paper, we have designed and implemented clustering classification systems-unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images' formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.


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Cite this article
[IEEE Style]
J. I. Kim and D. Y. Kim, "A Design of Clustering Classification Systems using Satellite Remote Sensing Images Based on Design Patterns," The KIPS Transactions:PartB , vol. 9, no. 3, pp. 319-326, 2002. DOI: 10.3745/KIPSTB.2002.9.3.319.

[ACM Style]
Jin Il Kim and Dong Yeon Kim. 2002. A Design of Clustering Classification Systems using Satellite Remote Sensing Images Based on Design Patterns. The KIPS Transactions:PartB , 9, 3, (2002), 319-326. DOI: 10.3745/KIPSTB.2002.9.3.319.