Classification-Based False Alarm Suppression for SAR Target Detection

False alarm suppression is becoming increasingly important as it directly impacts the reliability and efficiency of synthetic aperture radar (SAR) image detection systems. previous methods for false alarm suppression have focused primarily on identifying the motion properties of targets and removing the embedded noise. However, SAR images are captured in a single band, which means they lack continuous bands and dynamic information. In addition, noise removal often results in a significant loss of detail in the image. In this paper, we innovatively propose a classification-based false alarm suppression framework for SAR object detection, avoiding the need for motion identification and noise removal. In practice, we first train a classification network to categorize the SAR image slices into ocean, land, and offshore scenes. Based on the classification results, we then dynamically adjust the Intersection Over Union (IoU) threshold of Non-Maximum Suppression (NMS) in different scenes. Experimental results on a newly large multi-class target SAR dataset, MSAR-1.0, show that the false alarm rate decreased from 21% to 13%.