Remote sensing-based detection of dams in the Daqing River basin through optimization using hard negative samples of bridges
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.
damhard negative samplesDaqing River basinCenterNetobject detection
万方数据
维普
