SAR Target Recognition With Limited Training Data Based on Angular Rotation Generative Network

Abstract

Synthetic aperture radar (SAR) images are especially susceptible to the target aspect angles. For the SAR target recognition, the lack of training data at different aspect angles inevitably deteriorates the performance. To solve the problem, this letter introduces an angular rotation generative network (ARGN). It is actually an attribute-guided transfer learning method, and the shared attribute between the source and target domains is the target aspect angle. The aspect angle of the data for each type in the source domain covers in the range of 0°–360°, while the information of the aspect angle in the target domain is not complete. Assume that there is a mapping in the feature space between the two images of the same target under different aspect angles, and the mapping relation is learned from sufficient data in the source domain. Then, the mapping can also be applied in the target domain according to the idea of transfer learning. The learned knowledge contained in the feature space helps to improve the target recognition performance in the target domain. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set illustrate that our framework is efficient. For the three-class recognition of the MSTAR data set, the recognition rate is about 87% even when only 23 samples of each class are utilized as the training data. For a given target data, the generation results with counterclockwise rotation of 1°–90° for the aspect angle are performed. The qualitative and quantitative comparisons between the generated images and the real data are also displayed.