Abstract
Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The azimuth ambiguities are often caused by the sampling of the Doppler spectrum at finite intervals of the pulse repetition frequency (PRF) due to the acquisition mode of two channels [37]
The two-stage ship detection method is proposed in a complex background, i.e., in the offshore area
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ship detection plays a crucial role in maritime transportation, maritime surveillance applications in fishing, and maritime rights maintenance. Synthetic aperture radar (SAR), as active remote sensing, is most suitable for ship detection because it is sensitive to hard targets. SAR works throughout the day and in all weather conditions. Many SAR satellites, such as Radarsat1/2, TerraSAR-X, Sentinel-1, COSMO-SkyMed, and GF-3, have been providing a wide variety of SAR images with different resolutions, modes, and polarizations for maritime application, thereby enabling ship detection
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