面向光学遥感目标的全局上下文检测模型设计

张瑞琰; 姜秀杰; 安军社; 崔天舒

doi:10.37188/CO.2020-0057

Volume 13 Issue 6

Dec. 2020

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Chinese Optics > 2020 > 13(6): 1302-1313.

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

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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

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

doi: 10.37188/CO.2020-0057

ZHANG Rui-yan^{1, 2
,},
JIANG Xiu-jie^{1
,
,},
AN Jun-she¹,
CUI Tian-shu^{1, 2}

1.
Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: Supported by Laboratory Fund of Key Laboratory of Electronics and Information Technology for Space Systems, CAS (No. Y42613A32S)

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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

Abstract

Abstract

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.
- computer vision,
- object detection,
- remote sensing image,
- feature encoder-feature decoder,
- global-contextual feature

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References

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