Ship formation detection based on spatial distribution and attribute information

The existing target detection methods mostly focus on single-target. In remote sensing images, some natural or manmade targets often appear in groups or formations, such as ship formation. Ship formation not only contains the attribute information of single-ship, but also has the spatial distribution characteristics of formation. In this paper, a detection method for ship formation is proposed. The method mainly includes three stages: sub-target detection, formation extraction and formation association. In the first stage, the three features of the target's shape, gradient and texture are extracted by multi-feature fusion, on this basis, the sub-target is detected by support vector machine. In addition, the maximum symmetrical surround and spectral residual model are used to remove the possible interferences like ship-like reefs and cloud. In the second stage, agglomerative hierarchical clustering is adopted to obtain the ship formation information. Since the number of formations and the distribution of formation members are unknown, hierarchical clustering avoids the selection of cluster centers and the number of categories. In the last stage, by analyzing the spatial distribution and attribute information of ship formation, the topological features of ship formation are extracted and reconstructed based on spectral graph partitioning. Finally, combined with topological features and attribute information, the ship formation detection is realized by formation association. Experiments conducted on the simulation data set show that this method can detect ship formation effectively in the case of interferences, and is faster and more accurate than traditional fuzzy inference.