Automatic ship detection for optical satellite images based on visual attention model and LBP

Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, the problem is extremely difficult in the complex background, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust algorithm based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is simple, general, and not designed for specific types of images. Large-area images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship target or not, using a support vector machine method. Experimental results show the proposed method is insensitive to waves, clouds, and illumination, as well as high precision and low false alarms performance.