A semantic segmentation method for satellite image change detection

Jiahao Zhang; Bo Chen; Jianbang Zhou; Jingkun Yang; Zhong Chen; Jian Yang; Yanna Zhang

doi:10.1117/12.2538085

14 February 2020 A semantic segmentation method for satellite image change detection

Jiahao Zhang, Bo Chen, Jianbang Zhou, Jingkun Yang, Zhong Chen, Jian Yang, Yanna Zhang

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290H (2020) https://doi.org/10.1117/12.2538085
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Jiahao Zhang, Bo Chen, Jianbang Zhou, Jingkun Yang, Zhong Chen, Jian Yang, and Yanna Zhang "A semantic segmentation method for satellite image change detection", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290H (14 February 2020); https://doi.org/10.1117/12.2538085

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