Volume 13 Issue 6
Dec.  2020
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ZHANG Rui-yan, JIANG Xiu-jie, AN Jun-she, CUI Tian-shu. Design of global-contextual detection model for optical remote sensing targets[J]. Chinese Optics, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057
Citation: ZHANG Rui-yan, JIANG Xiu-jie, AN Jun-she, CUI Tian-shu. Design of global-contextual detection model for optical remote sensing targets[J]. Chinese Optics, 2020, 13(6): 1302-1313. doi: 10.37188/CO.2020-0057

Design of global-contextual detection model for optical remote sensing targets

doi: 10.37188/CO.2020-0057
Funds:  Supported by Laboratory Fund of Key Laboratory of Electronics and Information Technology for Space Systems, CAS (No. Y42613A32S)
More Information
  • Corresponding author: jiangxj@nssc.ac.cn
  • Received Date: 07 Apr 2020
  • Rev Recd Date: 11 May 2020
  • Available Online: 22 Oct 2020
  • Publish Date: 01 Dec 2020
  • To improve the detection accuracy and reduce the complexity of optical remote sensing of target images with a complex background, a global context detection model based on optical remote sensing of targets is proposed. First, a feature encoder-feature decoder network is used for feature extraction. Then, to improve the positioning ability of multi-scale targets, a method that combines global-contextual features and target center local features is used to generate high-resolution heat maps. The global features are used to achieve the pre-classification of targets. Finally, a positioning loss function at different scales is proposed to enhance the regression ability of the model. Experimental results show that the mean average precision of the proposed model reaches 97.6% AP50 and 83.4% AP75 on the NWPU VHR-10 public remote sensing data set, and the speed reaches 16 PFS. This design can achieve an effective balance between accuracy and speed. It facilitates subsequent porting and application of the algorithm on the mobile device side, which meets design requirements.

     

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