Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (2): 259-268.doi: 10.12082/dqxxkx.2019.180519

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Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method

Yilan LIU1,2(), Xiaoxia HUANG1,*(), Hongga LI1, Ze LIU3, Chong CHENG3, Xin'ge WANG3   

  1. 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Ministry of Housing and Urban-Rural Development of the People's Republic of China, Beijing 100835, China
  • Received:2018-10-17 Revised:2018-12-03 Online:2019-02-20 Published:2019-01-30
  • Contact: Xiaoxia HUANG E-mail:18649052480@163.com;hxx@irsa.ac.cn
  • Supported by:
    National Key Research and Development Program of China, No.2017YFB0503905;The Project Supported by the Open of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Land and Resources, No.KF-2016-02-012, KF-2018-03-032

Abstract:

With the development of rural economic construction, the pollution problem caused by domestic waste and industrial solid waste has become increasingly prominent, which has become a key problem to restrict the construction of the new rural developing and ecological civilization. At present, the investigation and statistics of informal solid waste in rural areas mainly depend on the reports of departments of each township step by step, and the workload is large. So based on high-resolution remote sensing images, this paper combines Deep Learning model with Conditional Random Field model to the study of rural solid waste extracting, and explores a recognition and extraction model of rural solid waste based on Deep Convolution Neural Network. Due to the solid waste in images is characterized by small size, distribution ,fragmentation and so on, in order to improve the efficiency, the model is divided into two parts: Recognition and Extraction. In the first part, a Full-connected Convolution Network (CNN) is used to identify and judge solid wastes quickly, and the image blocks include the interesting regions are screened. In the second part, Conditional Random Field model (CRF) is added to the traditional Full Convolution Neural Network (FCN) to extract boundary of solid waste and improve the overall segmentation accuracy.According to the relevant reports about solid waste of some rural areas in Anhui and Shanxi province and the field inspection by the urban and rural planning and management center of the Ministry of Housing and Urban-Rural Construction, Compared with the test results of the model in this paper,the results show the recognition accuracy is 86.87%,the shape extraction accuracy is 89.84%,and the Kappa coefficient is 0.7851. So the recognition and extraction accuracy of the paper's method is proved to be superior to the traditional methods. At the same time, this method has been gradually applied to the investigation of informal solid waste in countryside in Chengdu, Lanzhou, Hebei and other provinces, and achieved satisfactory results.

Key words: High-resolution remote sensing image, deep Learning, solid waste, convolution neural network, conditional random field