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This paper analyzes the potentialities to classify vessels detected through optical and synthetic-aperture radar (SAR) satellite-borne platforms and estimate their motion. For classification, the discriminative power of a set of geometric features extracted from segmented remote-sensed images is evaluated by clustering data derived from a set of accurate footprints belonging to either tanker or cargo ships. The same procedure is repeated on a few dozens of real, remote-sensed optical images. Concerning velocity estimation, which in this context is based on the detection and analysis of the wake pattern generated by the ship motion, a discussion concerning the accuracy of the wake detection task is presented. In particular, since wake patterns are usually hard to detect, a method is proposed to enhance the wake signal-to-noise ratio, based on a dedicated pre-filtering stage. Results returned by the proposed method are compared with those obtained adopting a standard literature approach, eventually observing that the introduction of the pre-filtering stage improves the wake detection accuracy. A maritime surveillance system based on a pipeline of the modules described here represents a useful tool to support the authorities in charge of monitoring maritime traffic with safety, security and law enforcement purposes.

The main purpose of this paper is to describe a software platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade pipeline of several image processing algorithms that input Radar or Optical imagery captured by satellite-borne sensors and try to identify vessel targets in the scene and provide quantitative descriptors about their shape and motion. This platform is innovative since it integrates in its architecture heterogeneous data and data processing solutions with the goal of identifying navigating vessels in a unique and completely automatic processing streamline. More in detail, the processing chain consists of: (i) the detection of target vessels in an input map, (ii) the estimation of each vessel&rsquo, s most descriptive geometrical and scatterometric (for radar images) features, (iii) the estimation of the kinematics of each vessel, (iv) the prediction of each vessel&rsquo, s forthcoming route, and (v) the visualization of the results in a dedicated webGIS interface. The resulting platform represents a novel tool to counteract unauthorized fishing and tackle irregular migration and the related smuggling activities.