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Proceedings Paper

A semantic segmentation method for satellite image change detection
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Paper Abstract

We apply the semantic segmentation method in deep network to high precision satellite image change detection, and propose a network framework to improve the detection performance.We directly processed the image after registration, without the steps of radiometric correction, and avoided the tedious steps of manual feature design by traditional methods.We tried to use Unet and Deeplab v3 model to divide the change area, and added the structure of jumping connection on the basis of Deeplab network, which made the edge of the detection graph more accurate and improved the performance of the network.The test results show that this method is effective for detecting the change of highprecision remote sensing images.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290H (14 February 2020); doi: 10.1117/12.2538085
Show Author Affiliations
Jiahao Zhang, Huazhong Univ. of Science and Technology (China)
Bo Chen, Huazhong Univ. of Science and Technology (China)
Jianbang Zhou, Huazhong Univ. of Science and Technology (China)
Jingkun Yang, Huazhong Univ. of Science and Technology (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
Jian Yang, Institute of Aerospace Information Innovation (China)
Yanna Zhang, Henan Univ. (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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