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Proceedings Paper

The change detection of multi-temporal remote sensing images based on D-S algorithm
Author(s): Yuanyong Dian; Shenghui Fang
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Paper Abstract

Change Detection is one of the most popular topics in the field of Multi-temporal Remote Sensing (RS) applications. In this paper, a novel approach was introduced for the change detection of the urban area. This approach adopts the Dempster-Shafer(D-S) algorithm for feature fusion of the multi-temporal RS images. It, in the first place,,constructs difference images of pixel and context respectively. These two difference images present the features of changes in different scales. The pixel difference image is obtained by fusing the results of the subtraction operation and the division operation, while the context difference image is obtained by the image context. Secondly, by using the difference images, two evidences could be constructed. These evidences are not certain, but they can give more reliable combination result if considering the average support of the evidence to different subsets in the assignment framework. And based on these evidences, the criterion function could be established by the D-S theory. At last, an improved D-S algorithm is applied to fuse the two different features for detecting the change information of the RS images. An experiment, using the SPOT and TM images of Wuhan urban area, has compared the accuracy of edge detection by using the new fusion algorithm and the existent ones. The result shows that the method of improved D-S is solid and efficacious, which has preferable value in remote sensing applications.

Paper Details

Date Published: 15 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749224 (15 October 2009); doi: 10.1117/12.838620
Show Author Affiliations
Yuanyong Dian, Wuhan Univ. (China)
Huazhong Agricultural Univ. (China)
Shenghui Fang, 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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