Zou Lichuan, Zhang Hong, Wang Chao, Wu Fan, Gu Feng
Key Laboratory of Digital Earth Science, Aerospace information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Nov 21;20(22):6673. doi: 10.3390/s20226673.
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.
在高分辨率合成孔径雷达（SAR）舰船检测中，SAR样本数量严重影响基于深度学习算法的性能。针对小样本下高分辨率舰船检测的应用需求，本文提出一种将改进的样本生成网络、多尺度瓦瑟斯坦辅助分类器生成对抗网络（MW-ACGAN）与Yolo v3网络相结合的高分辨率SAR舰船检测方法。首先，利用多尺度瓦瑟斯坦距离和梯度惩罚损失改进原始辅助分类器生成对抗网络（ACGAN），使改进后的网络能够稳定生成高分辨率SAR舰船图像。其次，在网络中添加多尺度损失项，增加多尺度图像输出层，从而生成多尺度SAR舰船图像。然后，将原始舰船数据集与生成的数据合并为一个复合数据集来训练Yolo v3目标检测网络，以解决小样本数据集下检测精度低的问题。高分三号（GF-3）3米SAR数据的实验结果表明，MW-ACGAN网络能够生成多尺度、多类别的舰船切片，ResNet18的置信度高于ACGAN网络，平均得分为0.91。Yolo v3网络模型的检测结果表明，由复合数据集训练的检测精度高达94%，远优于仅由原始SAR数据集训练的精度。这些结果表明，我们的方法能够充分利用原始数据集，提高舰船检测精度。
