农业机械学报2025，Vol.56Issue(9)：526-535,10.DOI:10.6041/j.issn.1000-1298.2025.09.043
基于深度学习和地理分析的淤地坝遥感识别
Remote Sensing Identification of Check Dams Based on Deep Learning and Geographic Analysis
摘要
Abstract
Accurately obtaining information on the number,location,and spatial distribution of check dams is fundamental for scientifically analyzing their erosion reduction and sediment trapping effects,as well as for future construction planning.Focusing on the Yanhe River Basin in the Loess Plateau,utilizing high-resolution imagery from GF-2 and Google Earth as data sources,deep learning object detection algorithms(Faster R-CNN,YOLO v3,Cascade R-CNN,and YOLOX)and semantic segmentation algorithms(FCN,U-Net,PSPNet,and DeepLab v3+)were employed to identify and extract the check dam bodies and dam lands.Geographic analysis methods and comprehensive interpretation were used to optimize the extraction results of the check dams.The results were as follows:in the identification of check dam bodies,the YOLOX model performed the best,achieving an average precision(AP)of 69.4％at an intersection of union(IoU)threshold of 0.50～0.95.Using this model to identify images of the Yanhe River Basin,totally 1 440 detection boxes were obtained,with an accuracy of 75.8％and a recall rate of 85.7％.In the extraction of dam lands,all four models performed well,with an average IoU,precision,recall,and F1 score all exceeded 0.85,90％,85％,and 88％,respectively.The majority voting method was used to combine the predictions of the four models,resulting in the extraction of 92.0 km2 of dam lands in the production and operation period and 10.6 km2 in the water storage and sediment trapping period.By incorporating geographic analysis methods,the accuracy of check dam body identification was increased from 75.8％to 84.5％,with the efficiency of identification and extraction doubling.These results indicated that the proposed method can be rapidly and accurately applied to the identification of check dams in the Loess Plateau,which was crucial for analyzing their impact on the ecological environment and for comprehensive management of small watersheds.关键词
淤地坝/深度学习/遥感/延河流域Key words
check dam/deep learning/remote sensing/Yanhe River Basin分类
农业科技引用本文复制引用
孙立全,郭家龙,苑紫岩,冯浩,吴淑芳..基于深度学习和地理分析的淤地坝遥感识别[J].农业机械学报,2025,56(9):526-535,10.基金项目
国家重点研发计划项目(2023YFD1900300) （2023YFD1900300）
