A Novel Sequence Modeling Network for Multiview SAR Target Recognition

Abstract

Synthetic aperture radar (SAR) is an active remote sensing system that utilizes radar to produce images of the Earth's surface. Due to its ability to operate under diverse weather conditions and throughout the day, SAR has gained significant attention in both civilian and military domains. The utilization of multiview SAR sequences enables the acquisition of a more comprehensive range of information than a single image, and facilitates adaptation to diverse scenarios, thereby enhancing the ability to accommodate variations in samples. Drawing inspiration from the Transformer architecture, this article proposes a multiview SAR target recognition method, called Res-Xformer, that not only deconstructs the deep learning procedure into single image feature extraction and sequence feature fusion but also divides the task of sequence feature extraction into sequence information fusion and feature channel fusion. Different from the Transformers focusing on the attention mechanism to fuse sequence information, alternative fusion methods such as multilayer perceptron (MLP) and pooling are also proposed in this study. Experimental results using the Moving and Stationary Target Acquisition and Recognition dataset demonstrate that the proposed method performs well across various operational conditions, with MLP and pooling as sequence token mixers yielding comparable performance to the attention mechanism.

Keywords

• Multiview
• Res-Xformer
• sequence feature fusion
• sequence modeling architecture
• synthetic aperture radar (SAR)
• token mixer