Azimuth-Sensitive Object Detection of High-Resolution SAR Images in Complex Scenes by Using a Spatial Orientation Attention Enhancement Network

The scattering features of objects in synthetic aperture radar (SAR) imagery are highly sensitive to different azimuth angles, and detecting azimuth-sensitive objects in complex scenes becomes a challenging task. To address this issue, we propose a novel framework called the spatial orientation atte...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (9), p.2198
Main Authors: Ge, Ji, Wang, Chao, Zhang, Bo, Xu, Changgui, Wen, Xiaoyang
Format: Article
Language:English
Subjects:
Accuracy
Agglomeration
Aircraft
Aircraft detection
Algorithms
Artificial intelligence
attention mechanisms
Azimuth
Classification
Complexity
Computer applications
Computer networks
Deep learning
Feature extraction
Image enhancement
Image resolution
Localization
Neural networks
object detection
Object recognition
Orientation
Radar imaging
Synthetic aperture radar
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Summary:The scattering features of objects in synthetic aperture radar (SAR) imagery are highly sensitive to different azimuth angles, and detecting azimuth-sensitive objects in complex scenes becomes a challenging task. To address this issue, we propose a novel framework called the spatial orientation attention enhancement network (SOAEN) by using aircraft detection in complex scenes of SAR imagery as a case study. Taking YOLOX as the basic framework, this framework introduces the inverted pyramid ConvMixer network (IPCN), the spatial-orientation-enhanced path aggregation feature pyramid network (SOEPAFPN), and the anchor-free decoupled head (AFDH) to achieve performance improvement. A spatial orientation attention module is proposed and introduced into the path aggregation feature pyramid network to form a new structure, the SOEPAFPN, for capturing feature transformations in different directions, highlighting object features and suppressing background effects; the IPCN is adapted to replace the backbone network of YOLOX for enhancing the multiscale feature extraction capability and reducing the computational complexity, while the AFDH is used to decouple object localization and classification to improve the efficiency and accuracy of object localization and classification. The experimental results of the multiple real complex scenes on Gaofen-3 1 m images show that the proposed method achieves the highest detection accuracy, with an average detection rate of 91.22% compared with the YOLO series networks.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14092198