Variational approximate inferential probability generative model for ship recognition using remote sensing data

Abstract Aiming at detecting sea targets reliably and timely, a discriminative ship recognition method using optical remote sensing data based on variational methods probability generative model is presented. First, an improved Hough transformation is utilized for pretreatment of the target candidate region, which reduces the amount of computation by filterring the edge points, our experiments indicate the targets (ships) can be detected quickly and accurately. Second, based on rough set theory, the common discernibility degree is used to compute the significance weight of each candidate feature and select valid recognition features automatically. Finally, for each node, its neighbor nodes are sorted by their manifold similarity to the node. Using the classes of the selected nodes from top of sorted neighbor nodes list, a dynamic probability generative model is built to recognize ships in data from optical remote sensing system. Experimental results on real data show that the proposed approach can get better classification rates at a higher speed than the k-nearest neighbor (KNN), support vector machines (SVM) and traditional hierarchical discriminant regression (HDR) method.

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