LAUSR

Due to the difficulties of obtaining sufficient real synthetic aperture radar (SAR) images, introducing simulated images can effectively enrich the training dataset in SAR target recognition. This letter explores how to accurately identify the targets in real SAR images by only using simulated training images. The key challenge is that there are distribution differences between the simulated (training) and real (test) data, which limits the performance of recognition methods. To solve this problem, a hierarchical recognition method is proposed. A well-trained convolutional neural network (CNN) is first utilized to pre-classify all the test images. Then, the focus of our proposed method is to find the hard test samples that are easy to be misclassified according to the CNN classification confidence and re-classify them. In fact, the distribution of these samples is relatively inconsistent with the distribution of the training data. Thus, we propose a multi-similarity fusion (MSF) classifier to re-classify them by comprehensively measuring the correlation between the hard samples and the training images through five similarity measures. During the fusion process, the bagging ensemble technique is used and the similarity measures are sampled to generate different subsets to enhance the diversity of sub-classifiers, thus the performance is improved. A large number of experiments finally verify the robustness and accuracy of the proposed method.