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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 90-97     DOI: 10.6046/gtzyyg.2020.03.12
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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
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Abstract  

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.
Keywords high resolution remote sensing images      convolutional neural network      SU-RetinaNet      informal garbage dumps detection     
:  P237  
Corresponding Authors: PENG Ling     E-mail: tongw_indus@126.com;pengling@aircas.ac.cn
Issue Date: 09 October 2020
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Tong WU
Ling PENG
Yuan HU
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Tong WU,Ling PENG,Yuan HU. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.12     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/90
Fig.1  Informal garbage dumps detection process based on SU-RetinaNet
Fig.2  RetinaNet network structure
Fig.3  Training data and composite samples of informal garbage dumps
Fig.4  Flow chart of the proposed method
Fig.5  Initial detection results of informal garbage dumps on test set
阶段 特征提取网络深度 α=0.25,
γ=1		 α=0.25,
γ=2		 α=0.25,
γ=5		 α=0.5,
γ=1		 α=0.5,
γ=2		 α=0.5,
γ=5		 α=0.75,
γ=1		 α=0.75,
γ=2		 α=0.75,
γ=5
阶段1 ResNet50 68.26 74.47 76.88 75.71 75.03 79.38 72.62 75.98 75.63
ResNet101 72.52 72.09 74.25 70.84 72.49 78.78 70.93 77.09 75.46
ResNet152 75.28 77.48 68.85 73.33 73.84 74.80 71.65 76.22 74.98
阶段2 ResNet50 81.39 82.34 84.54 82.93 85.18 85.38 84.28 87.25 85.98
ResNet101 81.49 83.76 83.17 78.99 83.77 83.59 79.97 82.03 83.26
ResNet152 79.89 82.6 79.32 80.35 84.33 83.18 77.7 82.41 84.08
Tab.1  Comparison of AP on the validation set under different parameter configurations using RetinaNet (%)
学习率 AP/%
0.000 50 78.66
0.000 10 87.25
0.000 05 86.62
0.000 01 86.48
Tab.2  Influence of different learning rate on AP
Fig.6  Final detection results of informal garbage dumps on test set
方法 AP/% 平均检测时间/s
DPM 42.00 30.348
R-CNN 66.00 8.636
Faster R-CNN 80.70 0.108
RetinaNet 77.12 0.097
SU-RetinaNet 85.92 0.097
Tab.3  Detection performance comparison of 5 different object detection methods
[1] 贾丽娟. 洱海北部流域有机固体废物氮磷污染及其控制对策研究[D]. 昆明:昆明理工大学, 2013.
[1] Jia L J. Pollution of nitrogen and phosphorus of rural organic solid waste and its control in northern Erhai-Lake Basin[D]. Kunming:Kunming University of Science and Technology, 2013.
[2] Bagheri S, Hordon R M. Hazardous waste site identification using aerial photography:a pilot study in Burlington County,New Jersey,USA[J]. Environ Manage, 1988,12(3):411-412.
doi: 10.1007/BF01867531 url: http://link.springer.com/10.1007/BF01867531
[3] Silvestri S, Omri M. A method for the remote sensing identification of uncontrolled landfills:formulation and validation[J]. International Journal of Remote Sensing, 2008,29(4):975-989.
doi: 10.1080/01431160701311317 url: https://www.tandfonline.com/doi/full/10.1080/01431160701311317
[4] Cadau E G, Putignano C, Laneve G, et al. Optical and SAR data synergistic use for landfill detection and monitoring.The SIMDEO project:Methods,products and results [C]//Geoscience and Remote Sensing Symposium.Quebec:IEEE, 2014: 4687-4690.
[5] Yan W Y, Mahendrarajah P, Shaker A, et al. Analysis of multi-temporal landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites[J]. Environmental Monitoring & Assessment, 2014,186(12):8161-73.
pmid: 25150051 url: https://www.ncbi.nlm.nih.gov/pubmed/25150051
[6] 刘亚岚, 任玉环, 魏成阶, 等. 北京1号小卫星监测非正规垃圾场的应用研究[J]. 遥感学报, 2009,13(2):320-326.
doi: 10.11834/jrs.20090255 url: http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=20090255&flag=1
[6] Liu Y L, Ren Y H, Wei C J, et al. Study on monitoring of informal open-air solid waste dumps based on Beijing-1 images[J]. Journal of Remote Sensing, 2009,13(2):320-326.
doi: 10.11834/jrs.20090255 url: http://www.jors.cn/jrs/ch/reader/view_abstract.aspx?file_no=20090255&flag=1
[7] 张方利, 杜世宏, 郭舟. 应用高分辨率影像的城市固体废弃物提取[J]. 光谱学与光谱分析, 2013,33(8):2024-2030.
pmid: 24159838
[7] Zhang F L, Du S H, Guo Z. Extracting municipal solid waste dumps based on high resolution images[J]. Spectroscopy and Spectral Analysis, 2013,33(8):2024-2030.
pmid: 24159838 url: https://www.ncbi.nlm.nih.gov/pubmed/24159838
[8] 秦海春. 基于国产高分遥感影像的城镇生活垃圾监管方法研究[J].中国建设信息化, 2016(4):75-77.
[8] Qin H C. Research on urban domestic waste supervision method based on domestic high score remote sensing image[J].Informatization of China Construction 2016(4):75-77.
[9] 王晨, 殷守敬, 孟斌, 等. 京津冀地区非正规垃圾场地遥感监测分析[J]. 高技术通讯, 2016,26(8):799-807.
[9] Wang C, Yin S J, Meng B, et al. Analysis of the non-regular garbage sites in the region of Beijing,Tianjin and Hebei using remote sensing monitoring images[J]. Chinese High Technology Letters 2016,26(8):799-807.
[10] Girshick R, Donahue J, Darrelland T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE, 2014: 580-587.
[11] Ren S, He K M, ROSS G, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015,39(6):1137-1149.
doi: 10.1109/TPAMI.2016.2577031 pmid: 27295650 url: https://www.ncbi.nlm.nih.gov/pubmed/27295650
[12] Redmon J, Divvala S, Girshick R, et al. You only look once:unified,real-time object detection [C]//Lucidyne Technologies.2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE, 2016: 1506-02640.
[13] Liu W, Anguelov D, Erhan D, et al. SSD:single shot MultiBox detector [C]//Robby Tan.2016 European Conference on Computer Vision.Amsterdam:ECCV, 2016: 21-37.
[14] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018: 318-327.
doi: 10.1109/TPAMI.2005.58 pmid: 15747788 url: https://www.ncbi.nlm.nih.gov/pubmed/15747788
[15] Hu Y, Li X, Zhou N, et al. A sample update-based convolutional neural network framework for object detection in large-area remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2019,16(6):947-951.
[16] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition [C]//Lucidyne Technologies.Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE, 2016: 770-778.
[17] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection [C]//Lucidyne Technologies.Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE, 2017: 2117-2125.
[18] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C]//Lucidyne Technologies.Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE, 2015: 3431-3440.
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