Abstract
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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Subject
General Earth and Planetary Sciences
Reference52 articles.
1. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources;Zhu;IEEE Geosci. Remote Sens. Mag.,2017
2. Sparse Synthetic Aperture Radar Imaging from Compressed Sensing and Machine Learning: Theories, Applications and Trends;Xu;IEEE Geosci. Remote Sens. Mag.,2022
3. Detection performance of a mean-level threshold;Steenson;IEEE Trans. Aerosp. Electron. Syst.,1968
4. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates;Finn;RCA Rev.,1968
5. Hansen, V.G. (1973, January 23–25). Constant false alarm rate processing in search radars. Proceedings of the IEEE Conference Publication No. 105, “Radar-Present and Future”, London, UK.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献