Space-borne SAR high-resolution inshore ship target identification based on a YOLO recognition and correction approach

Abstract

High-resolution space-borne synthetic aperture radar (SAR) images are important sources of information for marine monitoring and target recognition. For inshore port scenes, there is a wide variety of ship targets; moreover, as the scattering intensity of the land background is much higher than that of the sea surface, similar structures on land in SAR images can cause significant interference to the identification of ship targets, resulting in a high false alarm rate and false recognition rate. Therefore, the classification of ship targets in SAR images still requires professional interpretation and correction to eliminate possible false alarms and recognitions. In this article, by imitating the basic structural requirements for manual discrimination of target categories, a space-borne SAR high-resolution inshore ship classification and correction method of “Prerecognition—Fine Recognition—Matching Correction” is designed based on the YOLO recognition network. Taking three types of typical ship targets as examples, a high-resolution SAR inshore ship target dataset and a high-resolution SAR ship component architecture dataset are constructed to train the prerecognition network and the fine recognition network, respectively. The latter is based on the former to recognize the component composition of possible targets, and forms a component arrangement architecture vector. The final recognition results are obtained by matching and correcting the similarity distance with the three types of targets. This method can reduce the false alarm rate in ship target recognition in complex inshore port scenes to less than 10% and correct some false recognition results, thereby improving the accuracy of ship target identification and reducing the cost for manual correction.