Intelligence-based automatic detection and classification of ground collapses using object-based image analysis method: a case study in Paitan of Pearl River delta

Jie Dou; Xiao-zhan Zheng; Jun-ping Qian; Rui-hua Liu; Qi-tao Wu

doi:10.1117/12.813168

10 November 2008 Intelligence-based automatic detection and classification of ground collapses using object-based image analysis method: a case study in Paitan of Pearl River delta

Jie Dou, Xiao-zhan Zheng, Jun-ping Qian, Rui-hua Liu, Qi-tao Wu

Author Affiliations +

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 714623 (2008) https://doi.org/10.1117/12.813168
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

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.

Citation Download Citation

Jie Dou, Xiao-zhan Zheng, Jun-ping Qian, Rui-hua Liu, and Qi-tao Wu "Intelligence-based automatic detection and classification of ground collapses using object-based image analysis method: a case study in Paitan of Pearl River delta", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714623 (10 November 2008); https://doi.org/10.1117/12.813168

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