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Proceedings Paper

Semi-automatic typical collieries extraction based on remotely sensed imagery using active contour models
Author(s): Bin Liu; Guo Zhang; Jianya Gong; Xiaoyong Zhu; Wenbo Fei
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Paper Abstract

Keeping informed of the collieries exploitation is of great importance for the benefit of our country. Currently, with the high spatial resolution remotely sensed imagery, we are able to monitor the open air collieries directly without in situ inspections. This paper presents the application of active contour models(snakes) for semi-automatic extraction of the contour from the typical collieries in remote sensing images. After carefully examined the characteristics of the collieries, we improved the active contour model. The boundary of the collieries is not very clearly on the images, and the influence of the random image noise is extremely large. Therefore, improving the image power of Snake model is necessary. As the Snake model can only work on panchromatic images, we first fused the multi-spectral image to make full use of the spectral information contained with in the image. Then we used Canny edge detector which is anti-noise to extract the features. At the same time, a gauss filter is performed to the edge image to enlarge the envelope of the edge. We found that the image power calculated from the processed image is more efficient than it from the original image. A software package build in ArcGIS has been made based on this method.

Paper Details

Date Published: 13 October 2009
PDF: 7 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749206 (13 October 2009); doi: 10.1117/12.838367
Show Author Affiliations
Bin Liu, Wuhan Univ. (China)
Guo Zhang, Wuhan Univ. (China)
Jianya Gong, Wuhan Univ. (China)
Xiaoyong Zhu, Chinese Academy of Surveying and Mapping (China)
Wenbo Fei, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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