SAR target augmentation and recognition via cross-domain reconstruction

Abstract

The deep learning-based target recognition methods have achieved great performance in the preceding works. Large amounts of training data with label were collected to train a deep architecture, by which the inference can be obtained. For radar sensors, the data could be collected easily, yet the prior knowledge on label was difficult to be accessed. To solve the problem, a cross-domain re-imaging target augmentation method was proposed in this paper. The original image was first recast into the frequency domain. The frequency were then randomly filtered by a randomly generated mask. The size and the shape of mask was randomly determined. The filtering results were finally used for re-imaging. The original target can be then reconstructed accordingly. A series of new samples can be generated freely. The amounts and the diversity of dataset can be therefore improved. The proposed augmentation method can be implemented on-line or off-line, making it adaptable to various downstream tasks. Multiple comparative studies throw the light on the superiority of proposed method over the standard and recent techniques. It served to generate the images that would aid the downstream tasks.