A Novel Fusion Framework without Pooling for Noisy SAR Image Classification

Due to the particularity of SAR image, existing SAR image classification models often lack strong robustness against noise. Moreover, SAR images are naturally prone to speckle noise and sensitive to observed azimuth. To solve these problems, in this paper, we propose a novel fusion framework in which the convolutional layer with increased stride is used to replace the max pooling layer. Unlike max pooling layer roughly extracts the maximum pixel value in one region as its main feature, which is easy to introduce noise, convolution operation can update the weights and learn features more rationally by back-propagation. It also can achieve the same purpose of down sampling as pooling layers. In order to make full use of feature maps from different layers, our framework fuses the feature vectors extracted from different layers, which helps improve the performance of our classification model. For the problem of overfitting caused by the small MSTAR dataset of SAR images, we replace fully connected layers with convolution layers to relieve the overfitting of the convolution layers by reducing the number of parameters. In order to improve the robustness against observed azimuth angles of the dataset, we adopt the multi-channel calibration and superposition as model’s input, which can be used in real flight platform. The extensive experiments conducted on the MSTAR dataset have clearly demonstrated that our framework achieves higher classification accuracy, stronger robustness against noise than other existing methods, as well as its excellent classification performance for the targets of the same category and different subcategories, which is more difficult to be classified.