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Refined multi-scale feature-oriented object detection of the remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

    Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China

  • Email：shengzhang@cumt.edu.cn
  • Introduction：张省， 研究方向为遥感大数据及高性能计算。E-mail：shengzhang@cumt.edu.cn
ZHANG Sheng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：lishanshan@aircas.ac.cn
  • Introduction：李山山， 研究方向为遥感数据处理及应用。E-mail：lishanshan@aircas.ac.cn
LI Shanshan3*,  
  • Affiliation：

    Jinan Institute of Surveying and Mapping Survey, Jinan 250013, China

WEI Guofang4,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

ZHANG Xinnai1,  
  • Affiliation：

    Institute of Spacecraft Application System Engineering, Beijing 100094, China

GAO Jianwei5

résumé

Object detection of remote sensing image is the description of visual features of the object and the expression of the image prior knowledge, and the information obtained by the interpretation has a wide range of applications in both military and civilian fields. A refined multi-scale feature-oriented object detection of remote sensing image is proposed to address the problems of insufficient feature extraction capability of remote sensing image objects in complex scenes, large variations in object scales, arbitrary and closely arranged directions, and difficulties in the accurate orientation of horizontal frames used in traditional object detection.First, a contextual attention network based on dilated convolution is designed, which can capture local and global semantic information by using convolution kernels with different dilated rates and integrate semantic information into the original features utilizing an attention mechanism to enhance feature extraction. Second, a refined feature pyramid network is proposed to reduce the loss of channel information in the feature pyramid by pixel shuffling and strengthen the network’s ability to understand multi-scale object feature information with large variances. Finally, the study uses gliding vertices to regress the oriented rectangular box to represent the location of directed objects within remote sensing images.In this work, the effectiveness of the algorithm is verified by using Fast R-CNN OBB as a baseline on the object detection public datasets DOTA and HRSC2016. Results show that the algorithm in this work improves the mean average precision (mAP) by 22.65% on the DOTA dataset compared with the baseline. The final detection accuracy mAP reaches 76.78%. The final detection accuracy mAP on the HRSC2016 dataset reached 89.95%. In addition, the algorithm in this work has a better improvement compared with the various advanced algorithms.ConclusionFirst, the contextual attention network with dilated convolution is used to strengthen the object features, which enhances the discriminative ability of the convolutional neural network for objects and backgrounds in remote sensing images. Second, the refined feature pyramid is used to solve the problem of large variation of objects in remote sensing images. Finally, the direction factor of gliding vertices is introduced to represent the oriented objects, which reduces the regression boundedness problem that can be brought by angle regression.

mots-clés

remote sensing;deep learning;object detection;feature extraction;multi-scale feature pyramid;oriented bounding box

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