山东建筑大学学报2023，Vol.38Issue(6)：94-103,10.DOI:10.12077/sdjz.2023.06.013
基于Mask R-CNN的遥感影像滑坡检测方法研究
Research on landslides detection method using remote sensing images based on Mask R-CNN
摘要
Abstract
Rapid and accurate automatic landslides identification is of great significance to geological hazard survey,management and risk assessment.In this paper,Google Earth images are utilized to construct a sample set of historical landslides,and the Mask R-CNN object detection algorithm is employed to detect massive landslides automatically.The proposed method is demonstrated by two case studies in Danba County,Sichuan Province and Yongjing County,Gansu Province.Considering the factors which affect the occurrence of landslides,taking Danba County as an example,this paper uses the multi-layer perceptron mode to obtain the occurrence probability map of landslides.The accuracy of landslides detection is further validated by comparing the distribution of landslides detected by Mask R-CNN against the landslides occurrence probability map.The results show that the precision of landslide detection is 96%,recall is 85%,F1 score is 0.90,and 79%of the points are distributed in the area where the probability of landslides occurrence is higher than 80%;the precision of loess landslides detection is 98%,recall is 65%,and F1 score is 0.78.This method can effectively improve the automation of geological hazard information acquisition and the accuracy of landslides detection.关键词
滑坡识别/深度学习/Mask R-CNN/目标检测Key words
landslides identification/deep learning/Mask R-CNN/object detection分类
天文与地球科学引用本文复制引用
徐玲,刘晓慧,张金雨,刘震..基于Mask R-CNN的遥感影像滑坡检测方法研究[J].山东建筑大学学报,2023,38(6):94-103,10.基金项目
山东省高校科技计划项目(J16LH05) （J16LH05）
山东省自然科学基金项目(ZR2016DQ06) （ZR2016DQ06）
