SAR Target Recognition via Random Sampling Combination in Open-World Environments

Target recognition in SAR images was widely studied over the years. Most of these works were usually based on the assumption that the targets in the test set belong to a limited set of classes. In the practical scenarios, it is common to encounter various kinds of new targets. It is therefore more meaningful to study target recognition in open-world environments. In these scenes, it is needed to reject the unknown classes while maintain the classification performance on known classes. In the past years, few works were devoted to open set target recognition. Though the detection performance on unknown targets can be improved to a certain extent in the preceding works, most detection schemes are independent of a pretrained feature extractor, leading to potential open space risks. Besides, the model architectures are complicated, resulting in huge computational cost. To solve these problems, a family of new methods for open set target recognition is proposed. Targets indistinguishable from known classes are constructed by random sampling combination strategy. They are further sent into the classifier for feature learning. The original open-world environment is then transformed into a closed-world environment containing the unknown class. Moreover, the special implication of generated unknown targets is highlighted and used to realize unknown detection. Extensive experimental results on the MSTAR benchmark dataset illustrate the effectiveness of the proposed methods.