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Detection of water conservancy facilities in large-format image combining E-YOLO algorithm and NDWI constraint

  • role： First author第一作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xuzeyu@aircas.ac.cn
  • Introduction：许泽宇，研究方向为遥感信息计算。E-mail： xuzeyu@aircas.ac.cn
XU Zeyu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：shenzf@aircas.ac.cn
  • Introduction：沈占锋，研究方向为遥感信息计算。E-mail： shenzf@aircas.ac.cn
SHEN Zhanfeng13*,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Yang14,  
  • Affiliation：

    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China

    Key Laboratory of GIS and RS Application, Xinjiang Uygur Autonomous Region, Ürümqi 830011, China

LI Junli56,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

WANG Haoyu12,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Shuo14,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

JIAO Shuhui12,  
  • Affiliation：

    National Disaster Reduction Center of China, Beijing 100124, China

LI Lingling7

resumen

Water facilities play an important role in water scheduling, ecological protection and restoration of natural wetlands, utilization of resources and functions, and development. The traditional methods of statistics on the location and count of water conservancy facilities rely on compiled data, which has disadvantages such as time-consuming, untimely data update, and unknown specific geographic locations. Remote sensing provides new possibilities for large-scale detection of water conservancy facilities. Aiming at the problem of detection of water conservancy facilities with remote sensing images, this study proposes a large-scale image detection algorithm. Based on the YOLO v3 network and the characteristics of water conservancy facilities, the study was divided into two main aspects: (1) We improved the YOLO algorithm and obtained the E-YOLO algorithm. We proposed a PPA feature fusion method and a four-feature map cross prediction method with proportional prediction box to optimize the problems of small samples. Besides, we improved the loss function by highlighting the loss of confidence. In addition, we used the transfer learning method to read part of the feature extraction parameters of the pre-trained model. (2) With the improved E-YOLO algorithm as the core, a large-area water conservancy facility detection algorithm combined with the water body index constraint was obtained. Aiming at the problem of large image size with a small target scale, we used the water body index to constrain the sliding step to reduce the missed detection rate and false detection rate at the same time. Then we combined the network output with the contour merging method to optimize the detection results. We used the GF-2 data for this study. The experimental results show that: the E-YOLO algorithm can significantly improve the detection effect of water conservancy facilities. Compared with YOLO v3, the average F2 score of E-YOLO is increased by 1.25% and the E-YOLO algorithm has a better stability. The large-area detection method constrained by the water index can improve the detection accuracy while ensuring efficiency. Compared with the large-step and small-step methods, its F2 accuracy is increased by 3.72% and 2.70%, respectively. Our method provides a good solution for the detection of water conservancy facilities.

palabra clave

water conservancy facilities;remote sensing detection;E-YOLO;large image;NDWI

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