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Journal of Applied Remote Sensing • Open Access

Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion
Author(s): Chao Wang; Mengxi Xu; Xin Wang; Shengnan Zheng; Zhenli Ma

Paper Abstract

Aiming at the difficulties in change detection caused by the complexity of high-resolution remote sensing images that exist in varied ecological environments and artificial objects, in order to overcome the limitations in traditional pixel-oriented change detection methods and improve the detection precision, an innovative object-oriented change detection approach based on multiscale fusion is proposed. This approach introduced the classical color texture segmentation algorithm J-segmentation (JSEG) to change detection and achieved the multiscale feature extraction and comparison of objects based on the sequence of J-images produced in JSEG. By comprehensively using the geometry, spectrum, and texture features of objects, and proposing two different multiscale fusing strategies, respectively, based on Dempster/Shafer evidence theory and weighted data fusion, the algorithm further improves the divisibility between changed and unchanged areas, thereby establishing an integrated framework of object-oriented change detection based on multiscale fusion. Experiments were performed on high-resolution airborne and SPOT 5 remote sensing images. Compared with different object-oriented and pixel-oriented detection methods, results of the experiments verified the validity and reliability of the proposed approach.

Paper Details

Date Published: 24 September 2013
PDF: 17 pages
J. Appl. Remote Sens. 7(1) 173696 doi: 10.1117/1.JRS.7.073696
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Chao Wang, Hohai Univ. (China)
Mengxi Xu, Nanjing Institute of Technology (China)
Xin Wang, Hohai Univ. (China)
Shengnan Zheng, Nanjing Institute of Technology (China)
Zhenli Ma, Hohai Univ. (China)


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