Spaceborne SAR ship detection based on low complexity convolution neural network

Spaceborne SAR (Synthetic Aperture Radar) ship detection has been widely used in the sea rescue, territorial security and so on. As the traditional detection methods still have some shortages like high false alarm rate, this paper introduces the convolutional neural network (CNN) that has a powerful characterization for the spaceborne SAR ship detection. Aiming at the accurate and rapid demand of SAR ship detection, it proposes a spaceborne SAR ship detection algorithm based on low complexity CNN. The algorithm first combines the characteristics of spaceborne SAR images, uses the ROI extraction method to achieve the target rough extraction, getting the suspicious target slices and their corresponding location information, then accurately classifies all the suspicious target slices by the constructed CNN with low complexity to determine the target of the ship so as to realize the target detection of the ship. The experimental results show that the algorithm can achieve accurate spaceborne SAR ship detection. Compared with the traditional two-parameter CFAR and the methods based on the existing network frameworks (LeNet, GoogLeNet), the proposed algorithm has better performance and shorter detection time, which can effectively reduce the missed rate and the false alarm rate.