Share Email Print
cover

Proceedings Paper

Intelligence-based automatic detection and classification of ground collapses using object-based image analysis method: a case study in Paitan of Pearl River delta
Author(s): Jie Dou; Xiao-zhan Zheng; Jun-ping Qian; Rui-hua Liu; Qi-tao Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, a new method is proposed by applying case-based reasoning technique for detecting the ground collapses. The study demonstrates that the high resolution remote sensing images are suitable for monitoring the ground collapses in the study area with karst relief. With the help of object-based image analysis method, the generic algorithm (GA) for optimizing the spatial, shape, spectral, hierarchy and textural features was used in the multi-scale image segmentation with the good fitness value, and then the case library was built for detecting the collapse. The case library is reusable for place-independent detection. The proposed method has been tested in the Pearl River Delta in south China. The result of ground-collapse detection is well.

Paper Details

Date Published: 10 November 2008
PDF: 12 pages
Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714623 (10 November 2008); doi: 10.1117/12.813168
Show Author Affiliations
Jie Dou, Guangzhou Institute of Geochemistry (China)
Guangzhou Institute of Geography (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Xiao-zhan Zheng, Guangzhou Institute of Geological Survey (China)
Jun-ping Qian, Guangzhou Institute of Geochemistry (China)
Sun Yat-sen Univ. (China)
Rui-hua Liu, Guangzhou Institute of Geography (China)
Qi-tao Wu, Guangzhou Institute of Geography (China)


Published in SPIE Proceedings Vol. 7146:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top