排水口
卷积神经网络
计算机科学
水准点(测量)
遥感
人工智能
环境科学
地质学
环境工程
大地测量学
作者
Yaohuan Huang,Chengbin Wu,Haijun Yang,Haitao Zhu,Mingxing Chen,Jian Yang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-14
被引量:2
标识
DOI:10.1109/tgrs.2021.3113901
摘要
Outfalls into rivers are the final gate of anthropogenic pollution flowing to receiving waters, which means that outfall surveys are significant to basin environmental protection and ecosystem health management. Unmanned aircraft systems (UAS) with high spatial resolution imagery have become important data for ongoing surveys of outfalls. However, outfalls retrieval from UAS imagery is inefficient to visual interpretation and a challenging task for traditional spectral-based and object-oriented classification methods given the problems of salt-and-pepper noise and scale selection. In this study, an improved geo-deep learning approach based on the faster region convolutional neural network (R-CNN) architecture (GDCNN-outfalls) is proposed for retrieving outfalls into rivers with UAS imagery. In the proposed method, three tactics—anchor size, region of interest (RoI), and hard negative mining—were adopted to optimize the benchmark Faster R-CNN application in outfalls retrieval. Meanwhile, a geo-classifier module with digital surface model (DSM) enhancement and a spatial activation function was integrated with the Faster R-CNN architecture to generate GDCNN-outfalls. The validation experiments indicated that GDCNN-outfalls improved the performance of Faster R-CNN in outfall retrieval by suppressing false positive (FPs) from 33.52% to 26.14% and increasing the F1 score from 0.72 to 0.75. The test results confirm the performance of GDCNN-outfalls with a recall of 79.3% and higher precision (48.4%) than that of Faster R-CNN (2.1%), also show the GDCNN-outfalls is ten times faster than visual interpretation. This study demonstrates that the combination of deep learning and UAS techniques can be a feasible solution to detect outfalls in outfall surveys.
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