ISPRS-Annals ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-V-3-2020-45-2020 EARTHQUAKE-DAMAGED REGIONS DETECTION FROM HIGH RESOLUTION IMAGE BASED ON SUPER-PIXEL SEGMENTATION AND DEEP LEARNING Liu C. 1 Sui H. 1 Peng Y. 1 Hua L. 2 Li Q. 3 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China College of Resources & Environment, Huazhong Agriculture University, Wuhan 430070, China Hubei Geomatics Information Center (Hubei Research Institute of Beidou Satellite Navigation Applied Technology), Wuhan 4300779, China 03 08 2020 V-3-2020 45 51 Copyright: © 2020 C. Liu et al. 2020 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/isprs-annals-V-3-2020-45-2020.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/isprs-annals-V-3-2020-45-2020.pdf

Accurate detection and automatic processing of earthquake-damaged regions is essential for effective rescue and post-disaster reconstruction. In this study, we proposed a Combined Super-pixel Segmentation and AlexNet Detection approach (CSSAD) for automatically extracting damaged regions from post-earthquake high-resolution images. Simple Linear Iterative Clustering (SLIC) algorithm was used to segment the high resolution images to obtain more homogeneous geo-objects. Multiscale samples database, which took the different scale effect of damaged regions into account, was constructed based on the geometric centre of each super-pixel. AlexNet, which achieved the automatic extraction of high-level features and accurate identification of target geo-objects, was used to detect the damaged regions. To enhance the localization accuracy, the output of AlexNet was further refined using super-pixel segmentations and masked out of shadow and vegetation. Compared with traditional method, the proposed approach effectively reduces the false and missed detection ratio at least 10 percent.