*Article* **ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR**

**Jinyu Bao, Xiaoling Zhang \*, Tianwen Zhang and Xiaowo Xu**

> School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; 201811011909@std.uestc.edu.cn (J.B.); twzhang@std.uestc.edu.cn (T.Z.); xuxiaowo@std.uestc.edu.cn (X.X.)

**\*** Correspondence: xlzhang@uestc.edu.cn

**Abstract:** Most existing SAR moving target shadow detectors not only tend to generate missed detections because of their limited feature extraction capacity among complex scenes, but also tend to bring about numerous perishing false alarms due to their poor foreground–background discrimination capacity. Therefore, to solve these problems, this paper proposes a novel deep learning network called "ShadowDeNet" for better shadow detection of moving ground targets on video synthetic aperture radar (SAR) images. It utilizes five major tools to guarantee its superior detection performance, i.e., (1) histogram equalization shadow enhancement (HESE) for enhancing shadow saliency to facilitate feature extraction, (2) transformer self-attention mechanism (TSAM) for focusing on regions of interests to suppress clutter interferences, (3) shape deformation adaptive learning (SDAL) for learning moving target deformed shadows to conquer motion speed variations, (4) semantic-guided anchor-adaptive learning (SGAAL) for generating optimized anchors to match shadow location and shape, and (5) online hard-example mining (OHEM) for selecting typical difficult negative samples to improve background discrimination capacity. We conduct extensive ablation studies to confirm the effectiveness of the above each contribution. We perform experiments on the public Sandia National Laboratories (SNL) video SAR data. Experimental results reveal the state-of-the-art performance of ShadowDeNet, with a 66.01% best *f* 1 accuracy, in contrast to the other five competitive methods. Specifically, ShadowDeNet is superior to the experimental baseline Faster R-CNN by a 9.00% *f* 1 accuracy, and superior to the existing first-best model by a 4.96% *f* 1 accuracy. Furthermore, ShadowDeNet merely sacrifices a slight detection speed in an acceptable range.

**Keywords:** video synthetic aperture radar (SAR); moving target; shadow detection; deep learning; false alarms; missed detections
