结合桥梁难分样本优化的大清河流域水坝遥感检测  

Remote sensing-based detection of dams in the Daqing River basin through optimization using hard negative samples of bridges

作　　者：郭勇[1] 张琳翔 许泽宇 蔡中祥[1] GUO Yong;ZHANG Linxiang;XU Zeyu;CAI Zhongxiang(Geospatial Information Institute,Information Engineering University,Zhengzhou 450052,China;National Engineering Research Center for Geomatics,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China)

机构地区：[1]信息工程大学地理空间信息学院,郑州450052 [2]中国科学院空天信息创新研究院国家遥感应用工程技术研究中心,北京100101

出　　处：《自然资源遥感》2024年第4期201-209,共9页Remote Sensing for Natural Resources

基　　金：新疆维吾尔自治区重点研发任务专项“强震次生地质灾害承灾体识别与受损评估研究”(编号:2022B03001-3);新疆第三次科学考察项目“新疆遥感动态监测系统及时序信息反演”(编号:2021xjkk1403)共同资助。

摘　　要：水坝的检测对于城市规划、生态环境评估等有着重要意义。目前基于遥感的水坝检测研究主要是基于样本集的算法改进或在小区域上的检测,缺乏在大尺度地学区域的实践应用。而在大区域中,水坝分布稀疏,地表存在更多的桥梁等地物会对水坝的检测形成显著干扰。为应对这一问题,该文以大清河流域为例,研究大尺度区域内的水坝遥感检测。该文研究主要分为2个阶段,第一阶段是将容易与水坝混淆的桥梁作为难分负样本(即容易产生假阳性的样本)参加训练,基于DIOR公开数据集改进适合于水坝提取的神经网络结构;第二阶段是基于优化后的网络以及大区域多源样本数据进行微调训练获取模型,并实现大清河区域的水坝检测。优化后的模型在第一阶段测试中水坝检测F1分数为0.783,在第二阶段大清河流域检测得到了330处水坝,其结果与现有公开的水坝空间分布数据集GRandD相符,且更为详细。结果表明,结合桥梁样本优化训练后的模型可以有效避免对桥梁的误提取,从而提高检测精度。The dam detection is crucial for urban planning,ecological environment assessment,and other purposes.Currently,research on remote-sensing-based dam detection mainly focuses on algorithm improvements using sample sets or small-scale localized detections,with a significant gap in practical applications over large-scale geographical regions.In large-scale regions,the sparse distribution of dams,along with the presence of more surface features such as bridges,significantly interferes with dam detection.To address this issue,this study explored the Daqing River basin as a case study to investigate remote sensing methods for dam detection in large-scale regions.This study consisted of two main phases.In the first stage,bridges,which are easily confused with dams,are considered hard negative samples(i.e.,samples prone to false positives)for training.The neural network structure suitable for dam detection was improved based on the DIOR open dataset.In the second phase,the detection model was developed through fine-scale tuning using the optimized network alongside multi-source sample data from the large Daqing River basin.Concurrently,dams within the Daqing River region were detected.The optimized model yielded dam detection F1 of 0.783 in the first phase of tests and identified 330 dams in the Daqing River basin during the second phase.These results align with the existing publicly available dam spatial distribution dataset GRandD,even providing more details.The results of this study indicate that the model,optimized using bridge samples,can effectively mitigate the incorrect extraction of bridges,thereby improving detection accuracy.

关 键 词：水坝 难分负样本 大清河流域 CenterNet网络 目标检测 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]