Video SAR Moving Target Detection Using Dual Faster R-CNN

Abstract

Video synthetic aperture radar (SAR) has shown great potentials in detection and tracking of slow ground moving targets. The classical shadow-aided detection was applied in video SAR, and most recently, the deep learning approach has been developed for shadow-aided moving target detection. This article presents a joint moving target detection approach for video SAR using a dual faster region-based convolutional neural network (Faster R-CNN), which algorithmically combines the shadow detection in the SAR image and the Doppler energy detection in the range-Doppler (RD) spectrum domain, and this new approach can suppress false alarm sufficiently. Video SAR image and its corresponding low resolution RD spectrum are fed into the developed dual Faster R-CNN. A correct detection can be achieved if the shadow of a moving target and its Doppler energy are simultaneously detected by paired region proposals, which are obtained by sharing the region proposals of two independent region proposal networks (RPNs). Therefore, the performance of moving target detection can be significantly improved by using diverse features in different domains. This proposed approach has been verified by both the simulated and real video SAR data. Compared to other classical methods, our approach exhibits a great detection performance in terms of fewer false alarms and acceptable missing alarms.

Keywords

• Deep learning
• ground moving target indication (GMTI)
• radar imaging
• shadow detection
• video synthetic aperture radar (SAR)