Aircraft Detection in SAR Images Based on Peak Feature Fusion and Adaptive Deformable Network

Author:

Xiao XiayangORCID,Jia Hecheng,Xiao Penghao,Wang HaipengORCID

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

Publisher

MDPI AG

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

1. Aircraft Motion Identification Using Sub-Aperture SAR Image Analysis and Deep Learning;KOREAN J REMOTE SENS;2024

2. Detection and Taxonomy of Aircraft using Synthetic Aperture Radar Imaging;2024 International Conference on Integrated Circuits and Communication Systems (ICICACS);2024-02-23

3. Aircraft Detection in SAR Images via Point Features;IEEE Geoscience and Remote Sensing Letters;2024

4. SAR Image Aircraft Target Recognition Based on Improved YOLOv5;Applied Sciences;2023-05-17

5. Aircraft Detection and Classification Based on Joint Probability Detector Integrated with Scattering Attention;IEEE Transactions on Aerospace and Electronic Systems;2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3