Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Abstract

At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

Scattering-Point Topology for Few-Shot Ship Classification in SAR Images

“…The technique proposed for few-shot SAR image classification, known as the spatial transformed prototypical network (ST-PN), has been introduced in the network [45], which incorporates spatial transformations in the prototypical network to enhance its ability to recognize new classes. A few-shot learning framework is based on the prototypical network with a limited number of training samples [46], allowing it to learn a classifier capable of recognizing new classes with only a limited number of examples. A technique utilizing a meta-learning approach with amortized variational inference has been proposed [47], which involves a meta-learning framework to train a model capable of quickly adapting to new classes with only a few examples.…”