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 f1 accuracy, in contrast to the other five competitive methods. Specifically, ShadowDeNet is superior to the experimental baseline Faster R-CNN by a 9.00% f1 accuracy, and superior to the existing first-best model by a 4.96% f1 accuracy. Furthermore, ShadowDeNet merely sacrifices a slight detection speed in an acceptable range.
Highlights
Synthetic aperture radar (SAR) is an advanced Earth observation remote sensing tool.Its active radar-based remote sensing ensures its all-day and all-weather working advantage compared with optical sensors [1,2,3]
The ap index of ShadowDeNet is slightly better than feature pyramid network (FPN), its f 1 index is far superior to FPN, i.e., 66.01% >> 61.05%, about a ~5% accuracy improvement
This paper proposes a novel deep learning network ShadowDeNet for the moving This paperdetection proposesfrom a novel deep
Summary
Its active radar-based remote sensing ensures its all-day and all-weather working advantage compared with optical sensors [1,2,3]. It has been widely applied in civil fields, such as marine exploration, forestry census, topographic mapping, land resources survey, and traffic control, as well as military fields, such as battlefield reconnaissance, war situation monitoring, radar guidance, and strike effect evaluation [4,5,6]. Video SAR provides continuous multi-SAR images of the target imaging area to dynamically monitor the target scene in real time It can continuously record the changes of the target area and exhibit the information from the time dimension through the form of visual active images, conducive to the intuitive interpretation of human eyes [7]. It is receiving extensive attention from increasing scholars [8,9,10]
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