A Deep Learning-Based Moving Target Detection Method by Combining Spatiotemporal Information for ViSAR

Shangqu Yan, Fatong Zhang, Yaowen Fu, Wenpeng Zhang, Wei Yang, Ruofeng Yu

Abstract: Video synthetic aperture radar (ViSAR) can produce continuous images with a high frame rate and contain the moving target’s shadow, which provides numerous advantages for detecting moving targets. In this letter, a novel moving target detection method based on convolutional neural network (CNN) is proposed. The proposed network has a 3-D convolutional encoding path, a 2-D convolutional decoding path, and a bridge path to efficiently capture the target’s shadow information and summarize the spatiotemporal features from raw continuous images to high-level semantics. Furthermore, based on coordinate attention (CA), a temporal tri-CA (TTCA) module is proposed to obtain key spatiotemporal features in ViSAR data. The validity of the proposed method has been confirmed through experiments with actual ViSAR data and shows superior performance in the suppression of false alarms and missing alarms.