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Vol. 177, Issue 8, August 2014, pp. 128-135

 

Bullet

 

Cluster Validity Classification Approaches Based on Geometric Probability and Application
in the Classification of Remotely Sensed Images
 

1 LI Jian-Wei, 2 LI Xiao-Wen, 3 MAO Zheng-Yuan, 4 KONG Xiang-Zeng

1 College of physics and information engineering, Fuzhou University, Fuzhou 350108 China
2 College of mathematics and computer science, Longyan University, Longyan 364012 China
3 Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center, Fuzhou University, Fuzhou 350002 China
4 School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, Northern Ireland, UK, BT37 0QB
1 Tel.: +86-13950285662

1 E-mail: lwticq@163.com

 

Received: 20 May 2014 /Accepted: 31 July 2014 /Published: 31 August 2014

Digital Sensors and Sensor Sysstems

 

Abstract: On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy.

 

Keywords: Two-dimensional rectangular area, Cluster validity, Cluster analysis, Geometric probability, Remote sensing image classification.

 

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