A new ship detection and classification method of spaceborne SAR images under complex scene

Satellite remote sensing technology has always received wide attention for its developing performance of earth observation. Ship detection and classification based on spaceborne SAR images has been an attractive and intractable topic because the wide sea area is too complex to detect and classify all the objective ships. In this paper, a new ship detection and classification method for complex sea surface is presented. It adopts the visual saliency detection method based on spectral residual to obtain the locations of the regions of interest(ROIs) containing ships. And the morphology filter is employed to exclude a part of false alarm targets (FATs). Then, the types of the ships are classified based on convolution neural network (CNN). Finally, the locations and types of ships in large sea SAR images are acquired. Experimental results based on measured spaceborne SAR images have shown the effectiveness and accuracy of the proposed method.