Probability-Based Binary Attribute Weighted Prediction Network for SAR Image Classification

The problem of insufficient samples has been limiting the performance of intelligent interpretation in Synthetic Aperture Radar (SAR) images. Humans have the ability to recognize new instances with only a few samples, indicating that attributes play a crucial role in recognition. Attributes can be shared across categories and provide a distinctive representation. Motivated by this fact, this paper proposes an attribute-guided network consisting of a base classifier (BC) and an attribute classifier (AC). Firstly, we design binary attributes for SAR objects to enable more distinct feature representations. Secondly, the images are mapped into a semantic embedding space by embedding the attribute vectors. Finally, the performance of few-shot classification in SAR images is improved by jointly optimizing the loss in both attribute space and deep feature space. Our attribute-based framework is validated through ablation experiments on the MSTAR dataset and a self-built SAR aircraft dataset.