Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet
WU Tong1,2,3(), PENG Ling1,2(), HU Yuan1,2,3
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China 3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
The improvement of urbanization level and the imperfection of waste treatment infrastructure in China have caused the problem that informal garbage stacking becomes more and more prominent. With the development of high resolution remote sensing images, macro and efficient management of informal garbage dumps becomes possible. Most of the existing researches use visual interpretation and traditional supervised classification methods to identify informal garbage dumps from high resolution remote sensing images. Such methods are very time-consuming, and it is difficult for them to mine deep features from data, therefore the detection accuracy is limited. Therefore, the authors used convolutional neural network, proposed an informal garbage dumps detection framework SU-RetinaNet from high resolution remote sensing image based on sample updated and RetinaNet, analyzed the impact of different parameters and network structure on the model detection performance, and compared the results of using DPM, R-CNN , Faster R-CNN, RetinaNet and SU-RetinaNet 5 algorithms for the performance of informal garbage dumps detection. Experimental results show that the use of SU-RetinaNet for informal garbage dumps detection can achieve average precision of 85.92% and a detection speed of 0.097 s per image. Compared with traditional method, SU-RetinaNet greatly improves the detection efficiency of informal garbage dumps, and provides a feasible technical solution for effective monitoring and management of municipal waste.
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2020, Vol. 32 
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