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Recognition Hydropower Stations from Remote Sensing Images by Multi-stage CNN Detection and Segmentation

  • Xiaowei Tan
  • Zhifeng XiaoEmail author
  • Weiping Shao
Conference paper
  • 17 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 657)

Abstract

Global energy Internet is China’s national strategy. However, due to the lack of public information of power energy infrastructure, it is difficult to investigate foreign power facilities through public information. Therefore, it is a feasible way to recognize power facilities through remote sensing images. In this paper, we aim to recognize hydropower stations efficiently and accurately from high-resolution remote sensing images. Traditional target detection methods need to extract target features from images manually, so the designed features are not very robust. In this paper, we propose a method to recognize hydropower stations by multi-stage DCNN. Concisely, our task includes two parts: (1) Preliminary detection of hydropower station. DCNN models are used as feature extractor to get a rough detection result of hydropower stations. (2) Extract water surface by segmentation. We utilize the spatial relationship between hydropower station and water surface to obtain the final recognition results. Compared with other detection methods of deep learning, our method uses multi-stage CNN to detect hydropower stations and water surface, respectively, and uses the spatial relationship between hydropower stations and water surface, so as to further improve the recognition accuracy. The results show that our proposed method has high accuracy and confidence.

Keywords

Object detection Water surface segmentation Multi-stage CNN Spatial relations 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhanChina
  2. 2.State Grid Zhejiang Electric Power Co. LTD.HangzhouChina

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