Ship target segmentation and detection in complex optical remote sensing image based on component tree characteristics discrimination

Under the application background of sea-surface target surveillance based on optical remote sensing image, automatic sea-surface ship target recognition with complicated background is discussed in this paper. The technology this article focused on is divided into two parts, feature classification training and component class discrimination. In the feature classification training process, large numbers of sample images are used for feature selection and classifier determination of ship targets and false targets. Component tree characteristics discrimination achieves extraction of suspected target areas from complicated remote sensing image, and their features are entered to Fisher for ship target recognition. Experimental results show that the method discussed in this paper can deal with complex sea surface environment, and can avoid the interference of cloud cover, sea clutter and islands. The method can effectively achieve ship target recognition in complex sea background. © Copyright SPIE.