Abstract
A Ship detection method was proposed in this paper by combining top-down recognition with bottom-up image segmentation, which will work on Synthetic Aperture Radar (SAR) images and Space-borne Optical (SO) images. There are two steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to ship deformation and background clutter. The improved Shape Context is used to generate a set of hypotheses of ship locations and figure ground masks, which have high recall and low precision rate. In the verification step, we first compute a set of feasible segmentations that are consistent with top-down ship hypotheses, and then we propose a False Positive Pruning (FPP) procedure to prune out false positives. We exploit the fact that false positive regions typically do not align with any feasible image segmentation. Experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many ship classes.
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Sreedevi, Y., Reddy, B.E. (2013). Ship Detection from SAR and SO Images. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_125
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DOI: https://doi.org/10.1007/978-81-322-0740-5_125
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
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