Nonlinear Ship Wake Detection in SAR Images Based on Electromagnetic Scattering Model and YOLOv5

Abstract

Traditional wake detection methods have been successfully applied to the detection of a simple linear ship wake. However, they cannot effectively detect nonlinear wake and weak wake under high sea state conditions, whereas the deep-learning-based detection method could play to its strengths in this respect. Due to the lack of sufficient measured SAR images of ship wake to meet the training requirement for deep learning method, this paper explores the method to detect the nonlinear ship wake by combining electromagnetic scattering model with deep learning technique. The composite scene model of the sea surface and its wake is established first, then the facet scattering distribution of the ship wake and the sea background is analyzed with the help of the electromagnetic scattering model, and the simulation of the wake SAR images under the sea background is finally accomplished based on the modulation model. Combined with the simulation results and measured wake SAR images, the sample database is constructed. The You Only Look Once Version five algorithm (YOLOv5) based on deep learning techniques is applied to detect the wake target in complex conditions such as different sea states, multiple targets, curvilinear wakes, and weak wakes. The result show that the YOLOv5 leads to an obvious higher detection efficiency with satisfactory accuracy. Moreover, the comparison between YOLOv5 and the traditional Radon transform method for detecting nonlinear wakes in a strong noise background shows that the proposed method is better than the traditional object detection model. Thus, the proposed scheme would be a practical tool to deal with the detection of nonlinear ship wake and weak wake in complex scenarios, which will be helpful to the further remote sensing investigation of the ship.

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

“…YOLOv5, YOLOX, and Faster R-CNN achieved mean average precision (mAP) at 0.5 thresholds of 0.777, 0.849, and 0.688, respectively; therefore, YOLO outperforms Faster R-CNN. Wang et al [ 19 ] used the YOLOv5 algorithm in shape wake detection in SAR imagery and achieved an mAP score in the range of 80.1–84.8%, proving that YOLOv5 can successfully be used in complex conditions, such as different sea states and multiple targets. Xu et al [ 20 ] trained the YOLOv5 algorithm for ship detection in large-scene Sentinel-1 SAR images, effectivly scoring mAP of 0.7315.…”

Simulation of Synthetic Aperture Radar Images for Ocean Ship Wakes

“…The initial observation of SAR ship wake images with significant features captured by SEASAT dates back to 1978 [8]. Since then, researchers have placed significant emphasis on the study of ship wake radar imaging as a method for ship detection and classification [9][10][11][12][13]. There are several compelling reasons why detecting the wake patterns or a combination of wakes and ships is superior to solely detecting the ships themselves.…”

Study on the Composite Scattering Characteristics of Ship Target on the Sea Surface with Its Wake

“…Sun et al [11] simulated nonlinear ship waves on an infinite fluid using a numerical scheme combining the boundary integral method with the Jacobian-free Newton Craycroft method, and elucidated the influence of ship size on Kelvin ship waves. To investigate the impact of ship parameters and speckle noise in numerically simulated SAR images, Wang et al [12] combined electromagnetic scattering models with deep learning techniques to explore nonlinear ship wake detection methods. Song et al [13] introduced wake effects into a polarization reflection distribution model for rough seas and combined polarized skylight distribution with rough sea wake reflection distribution to establish a polarization characteristic model for rough sea wakes.…”