基于多卷积神经网络融合的SAR舰船分类

针对SAR图像中小型舰船分类准确率较低的问题，提出一种多卷积神经网络加权融合的方法。首先构建高分辨率卷积神经网络对特征图进行多尺度融合，引入微调模型和标签平滑减少训练过拟合的问题；然后利用高分辨网络、MobileNetv2网络和SqueezeNet网络训练3种单分类模型；最后采用加权投票方式对3种分类模型的结果进行融合。采用融合算法对GF-3号舰船数据集进行分类实验，取得94.83%的准确率、95.43%的召回率和0.9513的F1分数的分类性能。实验结果表明，该舰船分类算法模型具有较优的分类能力，验证了其在高分辨率SAR图像舰船分类上的有效性。

SAR Ship Classification Based on Multi-convolutional Neural Network Fusion

The accuracy of small ship classification in Syntactic Aperture Radar （SAR） images is low. To solve the problem， a classification approach based on the weighted fusion of different convolutional neural network results is proposed. Firstly， a high-resolution convolutional neural network is constructed to conduct multi-scale feature fusion， fine-tuning model and label smoothing are introduced to reduce the problem of training over-fitting. Then three single classification models are trained using the high-resolution network， MobileNetv2 network and SqueezeNet network. Finally， the results of three classification models are fused by weighted voting. The fusion method is used to carry out classification experiment on GF-3 ship dataset， the results obtained are: precision 94.83%， recall rate 95.43%， F1 score 0.9513. Experimental results show that the algorithm model proposed in this paper has better classification ability， which verifies its effectiveness in high-resolution SAR image ship classification.