Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention

ZHANG Jing; WU Xinjia; ZHAO Xiaolei; ZHUO Li; ZHANG Jie

doi:10.11999/JEIT210466

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2924-2931

ZHANG Jing, WU Xinjia, ZHAO Xiaolei, ZHUO Li, ZHANG Jie. Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466

Citation:

ZHANG Jing, WU Xinjia, ZHAO Xiaolei, ZHUO Li, ZHANG Jie. Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466

Citation:

ZHANG Jing, WU Xinjia, ZHAO Xiaolei, ZHUO Li, ZHANG Jie. Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2924-2931. doi: 10.11999/JEIT210466

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Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention

doi: 10.11999/JEIT210466

ZHANG Jing^{1, 2
,
,},
WU Xinjia¹,
ZHAO Xiaolei¹,
ZHUO Li^{1, 2},
ZHANG Jie³

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
3.
Department of Resource Information Engineering, China University of Geosciences, Wuhan 430074, China

Funds: The National Natural Science Foundation of China (61370189), Beijing Municipal Education Commission Cooperation Beijing Natural Science Foundation (KZ201810005002), The General Program of Beijing Municipal Education Commission (KM202110005027)

Received Date: 2021-05-25
Rev Recd Date: 2021-09-01

Available Online: 2022-04-13

Publish Date: 2022-08-17

Abstract

Abstract

Ground objects in high-resolution remote sensing images are often closely related to the scene categories. If the constraint information of the scene on the ground object can be usefully employed, it is expected to improve further the performance of object detection. Considering the relationship between scene information and objects, a scene constrained object detection method in high-resolution remote sensing images by Relation-aware Global Attention (RGA) is proposed. First, the global scene features are learned by adding the global relational attention to the basic network in Feature fusion and Scaling-based Single Shot Detector (FS-SSD). Then, object is predicted by combining the oriented response convolution module with the multiscale feature module under the constraints of learned global scene features. Finally, two loss functions are used to optimize jointly the network to achieve object detection. Four experiments are conducted on NWPU VHR-10 dataset and better object detection performance is achieved under the constraints of scene information.
- High-resolution remote sensing image,
- Deep learning,
- Object detection,
- Scene constrain,
- Relation-aware global attention

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References(17)

References

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