针对合成孔径雷达(SAR)图像噪声较高,导致目标识别率较低的问题,选取MSTAR数据作为样本集,首先分析了对网络添加注意力机制的必要性,接着在残差注意力网络中引入残差收缩块,从识别率和参数量的角度进行实验分析。改进残差注意力网络的第一阶段和输出阶段,得到模型S,最终模型S的识别率达99.6%的同时,参数量减少了近1/2。为测试改进模型的鲁棒性,对图像进行了遮挡和加噪处理,结果显示,在图像被遮挡和有椒盐噪声情况下,模型S具有较强的鲁棒性。

In this study, MSTAR data were selected as a sample set to solve the problem of the high noise of the synthetic aperture radar (SAR) images leading to a low target recognition rate. First, the necessity of adding an attention mechanism to the network was analyzed. Subsequently, the residual shrinkage piece was introduced in the residual attention network. An experimental analysis was performed from the perspective of the recognition rate and number of parameters. Model S was obtained by improving the first stage and the output stage of the residual attention network. Consequently, the recognition rate of model S was found to be 99.6%, and the number of parameters was reduced by nearly 1/2. Image occlusion and noise processing were conducted to test the robustness of model S. Results show that model S has a strong robustness under the conditions of image occlusion, salt and pepper noise.