Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network
Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/6077 |
| _version_ | 1797462262118612992 |
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| author | Xiayang Xiao Hecheng Jia Penghao Xiao Haipeng Wang |
| author_facet | Xiayang Xiao Hecheng Jia Penghao Xiao Haipeng Wang |
| author_sort | Xiayang Xiao |
| collection | DOAJ |
| description | Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To cope with these problems, a novel approach called adaptive deformable network (ADN) combined with peak feature fusion (PFF) is proposed for aircraft detection. The PFF is designed for taking full advantage of the strong scattering features of aircraft, which consists of peak feature extraction and fusion. To fully exploit the strong scattering features of the aircraft in SAR images, peak features are extracted via the Harris detector and the eight-domain pixel detection of local maxima. Then, the saliency of aircraft under multiple imaging conditions is enhanced by multi-channel blending. All the PFF-preprocessed images are fed into the ADN for training and testing. The core components of ADN contain an adaptive spatial feature fusion (ASFF) module and a deformable convolution module (DCM). ASFF is utilized to reconcile the inconsistency across different feature scales, raising the characterization capabilities of the feature pyramid and improving the detection performance of multi-scale aircraft further. DCM is introduced to determine the 2-D offsets of feature maps adaptively, improving the geometric modeling abilities of aircraft in various shapes. The well-designed ADN is established by combining the two modules to alleviate the problems of the multi-scale targets and attitude sensitivity. Extensive experiments are conducted on the GaoFen-3 (GF3) dataset to demonstrate the effectiveness of the PFF-ADN with an average precision of 89.34%, as well as an F1-score of 91.11%. Compared with other mainstream algorithms, the proposed approach achieves state-of-the-art performance. |
| first_indexed | 2024-03-09T17:34:00Z |
| format | Article |
| id | doaj.art-9aef94653e0e4fdb8bbfafe3a4ad2033 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:34:00Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9aef94653e0e4fdb8bbfafe3a4ad20332023-11-24T12:05:23ZengMDPI AGRemote Sensing2072-42922022-11-011423607710.3390/rs14236077Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable NetworkXiayang Xiao0Hecheng Jia1Penghao Xiao2Haipeng Wang3Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaDue to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To cope with these problems, a novel approach called adaptive deformable network (ADN) combined with peak feature fusion (PFF) is proposed for aircraft detection. The PFF is designed for taking full advantage of the strong scattering features of aircraft, which consists of peak feature extraction and fusion. To fully exploit the strong scattering features of the aircraft in SAR images, peak features are extracted via the Harris detector and the eight-domain pixel detection of local maxima. Then, the saliency of aircraft under multiple imaging conditions is enhanced by multi-channel blending. All the PFF-preprocessed images are fed into the ADN for training and testing. The core components of ADN contain an adaptive spatial feature fusion (ASFF) module and a deformable convolution module (DCM). ASFF is utilized to reconcile the inconsistency across different feature scales, raising the characterization capabilities of the feature pyramid and improving the detection performance of multi-scale aircraft further. DCM is introduced to determine the 2-D offsets of feature maps adaptively, improving the geometric modeling abilities of aircraft in various shapes. The well-designed ADN is established by combining the two modules to alleviate the problems of the multi-scale targets and attitude sensitivity. Extensive experiments are conducted on the GaoFen-3 (GF3) dataset to demonstrate the effectiveness of the PFF-ADN with an average precision of 89.34%, as well as an F1-score of 91.11%. Compared with other mainstream algorithms, the proposed approach achieves state-of-the-art performance.https://www.mdpi.com/2072-4292/14/23/6077synthetic aperture radar (SAR)aircraft detectiondeep learningpeak featureadaptive spatial feature fusiondeformable convolution module |
| spellingShingle | Xiayang Xiao Hecheng Jia Penghao Xiao Haipeng Wang Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network Remote Sensing synthetic aperture radar (SAR) aircraft detection deep learning peak feature adaptive spatial feature fusion deformable convolution module |
| title | Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network |
| title_full | Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network |
| title_fullStr | Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network |
| title_full_unstemmed | Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network |
| title_short | Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network |
| title_sort | aircraft detection in sar images based on peak feature fusion and adaptive deformable network |
| topic | synthetic aperture radar (SAR) aircraft detection deep learning peak feature adaptive spatial feature fusion deformable convolution module |
| url | https://www.mdpi.com/2072-4292/14/23/6077 |
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