Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method

Abstract

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the “You Only Look Once (YOLO) v5 model”, which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships’ images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction.

Keywords

• Spotlight SAR image
• ship detection
• deep learning
• YOLOv5