Ship detection in high spatial resolution remote sensing image based on improved sea-land segmentation

A new method to detect ship target at sea based on improved segmentation algorithm is proposed in this paper, in which the improved segmentation algorithm is applied to precisely segment land and sea. Firstly, mean value is replaced instead of average variance value in Otsu method in order to improve the adaptability. Secondly, Mean Shift algorithm is performed to separate the original high spatial resolution remote sensing image into several homogeneous regions. At last, the final sea-land segmentation result can be located combined with the regions in preliminary sea-land segmentation result. The proposed segmentation algorithm performs well on the segment between water and land with affluent texture features and background noise, and produces a result that can be well used in shape and context analyses. Ships are detected with settled shape characteristics, including width, length and its compactness. Mean Shift algorithm can smooth the background noise, utilize the wave's texture features and helps highlight offshore ships. Mean shift algorithm is combined with improved Otsu threshold method in order to maximizes their advantages. Experimental results show that the improved sea-land segmentation algorithm on high spatial resolution remote sensing image with complex texture and background noise performs well in sea-land segmentation, not only enhances the accuracy of land and sea boarder, but also preserves detail characteristic of ships. Compared with traditional methods, this method can achieve accuracy over 90 percent. Experiments on Worldview images show the superior, robustness and precision of the proposed method.