Ship Detection and Recognition in Optical Remote Sensing Images Based on Scale Enhancement Rotating Cascade R-CNN Networks

Ship detection and recognition in remote sensing images have important significance in military and civilian applications. Traditional methods have insufficient generalization ability in complicated scenes. The Faster R-CNN-based methods cannot predict the orientation of the ship. The R2CNN-based methods can predict the orientation of the ship but not considerate the scale of object in classification. In order to solve the problems mentioned above, this article proposes a scale enhancement rotating Cascade R-CNN network (SER-Cascade). Using the multistage network of rotating Cascade R-CNN, the output of the previous stage is fed to current stage, which can effectively regress the orientation of the ship. To improve the recognition performance of multi-class ships, a novel RoI pooling method is proposed in this article, in which the scale information is enhanced and context information is reserved. To evaluate the proposed networks, a dataset named HR-SHIP-15 that currently contains 15 categories of ship targets has been produced for ship recognition. Experiments are conducted on HR-SHIP-15 dataset, and the results verify that the proposed method has state-of-the-art performance.