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A complete processing chain for ship detection using optical satellite imagery

Abstract : Ship detection from remote sensing imagery is a crucial application for maritime security, which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. In the framework of a European integrated project Global Monitoring for Environment and Security (GMES) Security/Land and Sea Integrated Monitoring for European Security (LIMES), we developed an operational ship detection algorithm using high spatial resolution optical imagery to complement existing regulations, in particular the fishing control system. The automatic detection model is based on statistical meth- ods, mathematical morphology and other signal-processing techniques such as the wavelet analysis and Radon transform. This article presents current progress made on the detection model and describes the prototype designed to classify small targets. The prototype was tested on panchromatic Satellite Pour l'Observation de la Terre (SPOT) 5 imagery taking into account the environmental and fishing context in French Guiana. In terms of automatic detection of small ship targets, the proposed algorithm performs well. Its advantages are manifold: it is simple and robust, but most of all, it is efficient and fast, which is a crucial point in performance evaluation of advanced ship detection strategies.
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Christina Corbane, Laurent Najman, Emilien Pecoul, Laurent Demagistri, Michel Petit. A complete processing chain for ship detection using optical satellite imagery. International Journal of Remote Sensing, Taylor & Francis, 2010, 31 (22), pp.5837--5854. ⟨10.1080/01431161.2010.512310⟩. ⟨hal-00833006⟩



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