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Resource-Based Port Material Yard Detection with SPPA-Net

Author

Listed:
  • Xiaoyong Zhang

    (Beijing Key Laboratory of High Dynamic Navigation, Beijing Information Science and Technology University, Beijing 100101, China)

  • Rui Xu

    (Beijing Key Laboratory of High Dynamic Navigation, Beijing Information Science and Technology University, Beijing 100101, China)

  • Kaixuan Lu

    (Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Zhihang Hao

    (Beijing Key Laboratory of High Dynamic Navigation, Beijing Information Science and Technology University, Beijing 100101, China)

  • Zhengchao Chen

    (Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Mingyong Cai

    (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
    Satellite Application Center for Ecology and Environment, MEE, Beijing 100094, China)

Abstract

Since the material yard is a crucial place for storing coal, ore, and other raw materials, accurate access to its location is of great significance to the construction of resource-based ports, environmental supervision, and investment and operating costs. Its extraction is difficult owing to its small size, variable shape, and dense distribution. In this paper, the SPPA-Net target detection network was proposed to extract the material yard. Firstly, a Dual-Channel-Spatial-Mix Block (DCSM-Block) was designed based on the Faster R-CNN framework to enhance the feature extraction ability of the location and spatial information of the material yard. Secondly, the Feature Pyramid Network (FPN) was introduced to improve the detection of material yards with different scales. Thirdly, a spatial pyramid pooling self-attention module (SPP-SA) was established to increase the global semantic information between material yards and curtail false detection and missed detection. Finally, the domestic GF-2 satellite data was adopted to conduct extraction experiments on the material yard of the port. The results demonstrated that the detection accuracy of the material yard reached 88.7% when the recall rate was 90.1%. Therefore, this study provided a new method for the supervision and environmental supervision of resource-based port material yards.

Suggested Citation

  • Xiaoyong Zhang & Rui Xu & Kaixuan Lu & Zhihang Hao & Zhengchao Chen & Mingyong Cai, 2022. "Resource-Based Port Material Yard Detection with SPPA-Net," Sustainability, MDPI, vol. 14(24), pages 1-12, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16413-:d:997112
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    References listed on IDEAS

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    1. Tonglai Liu & Ronghai Luo & Longqin Xu & Dachun Feng & Liang Cao & Shuangyin Liu & Jianjun Guo, 2022. "Spatial Channel Attention for Deep Convolutional Neural Networks," Mathematics, MDPI, vol. 10(10), pages 1-10, May.
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    Cited by:

    1. Ahmed Sahraoui & Nguyen Khoi Tran & Youssef Tliche & Ameni Kacem & Atour Taghipour, 2023. "Examining ICT Innovation for Sustainable Terminal Operations in Developing Countries: A Case Study of the Port of Radès in Tunisia," Sustainability, MDPI, vol. 15(11), pages 1-22, June.

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