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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.The automatic detection of aircrafts from SAR images is widely applied in both military and civil fields, but there are still considerable challenges. To address the high variety of aircraft sizes and complex background information in SAR images, a new fast detection framework based on convolution neural networks is proposed, which achieves automatic and rapid detection of aircraft with high accuracy. First, the airport runway areas are detected to generate the airport runway mask and rectangular contour of the whole airport are generated. Then, a new deep neural network proposed in this paper, named Efficient Weighted Feature Fusion and Attention Network (EW-FAN), is used to detect aircrafts. EWFAN integrates the weighted feature fusion module, the spatial attention mechanism, and the CIF loss function. EWFAN can effectively reduce the interference of negative samples and enhance feature extraction, thereby significantly improving the detection ac-curacy. Finally, the airport runway mask is applied to the detected results to reduce false alarms and produce the final aircraft detection results. To evaluate the performance of the proposed frame-work, large-scale Gaofen-3 SAR images with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EWFAN algorithm are 95.4% and 3.3%, respectively, which outperforms Efficientdet and YOLOv4. In addition, the average test time with the proposed framework is only 15.40 s, indicating satisfying efficiency of automatic aircraft detection.
Author(s): Wang J, Xiao H, Chen L, Xing J, Pan Z, Luo R, Cai X
Publication type: Article
Publication status: Published
Journal: Remote Sensing
Year: 2021
Volume: 13
Issue: 5
Pages: 1-21
Print publication date: 01/03/2021
Online publication date: 28/02/2021
Acceptance date: 23/02/2021
Date deposited: 09/04/2021
ISSN (electronic): 2072-4292
Publisher: MDPI AG
URL: https://doi.org/10.3390/rs13050910
DOI: 10.3390/rs13050910
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