A Novel False Alarm Suppression Method for CNN-Based SAR Ship Detector

Abstract

Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.