24 September 2013 Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion
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J. of Applied Remote Sensing, 7(1), 173696 (2013). doi:10.1117/1.JRS.7.073696
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.
Chao Wang, Mengxi Xu, Xin Wang, Shengnan Zheng, Zhenli Ma, "Object-oriented change detection approach for high-resolution remote sensing images based on multiscale fusion," Journal of Applied Remote Sensing 7(1), 173696 (24 September 2013). http://dx.doi.org/10.1117/1.JRS.7.073696
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KEYWORDS
Image fusion

Remote sensing

Data fusion

Detection and tracking algorithms

Reliability

Algorithms

Feature extraction

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