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Open AccessArticle

MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection

by 1,2, 1,*, 1,2, 1 and 1,2
1
Key Laboratory of Digital Earth Science, Aerospace information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6673; https://doi.org/10.3390/s20226673
Received: 6 October 2020 / Revised: 11 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Section Remote Sensors)
In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection. View Full-Text
Keywords: high-resolution SAR; Ship detection; MW-ACGAN; Yolo v3 high-resolution SAR; Ship detection; MW-ACGAN; Yolo v3
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Zou, L.; Zhang, H.; Wang, C.; Wu, F.; Gu, F. MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection. Sensors 2020, 20, 6673.

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