This paper presents a Ship Monitoring System (SIMONS) working with Synthetic Aperture Radar (SAR) images. It is able to infer ship detection and classification information, and merge the results with other input channels, such as polls from the Automatic Identification System (AIS). Two main stages can be identified, namely: SAR processing and data dissemination. The former has three independent modules, which are related to Coastline Detection (CD), Ship Detection (SD) and Ship Classification (SC). The later is solved via an advanced web interface, which is compliant with the OpenSource standards fixed by the Open Geospatial Consortium (OGC). SIMONS has been designed to be a modular, unsupervised and reliable system that meets Near-Real Time (NRT) delivery requirements. From data ingestion to product delivery, the processing chain is fully automatic accepting ERS and ENVISAT formats. SIMONS has been developed by GMV Aerospace, S.A. with three main goals, namely: 1) To limit the dependence on the ancillary information provided by systems such as AIS.2) To achieve the maximum level of automatism and restrict human manipulation.3) To limit the error sources and their propagation. Spanish authorities have validated SIMONS. The results have been satisfactory and have confirmed that the system is useful for improving decision making. For single-polarimetric images with a resolution of 30 m, SIMONS permits the location of ships larger than 40 m with a classification ratio around 50% of positive matches. These values are expected to be improved with SAR data from new sensors. In the paper, the performance of SD and SC modules is assessed by cross-check of SAR data with AIS reports.
This paper presents a new ship monitoring system developed at GMV Aerospace that integrates the reports provided by the Automatic Identification System (AIS) with ship-related information derived from SAR data analysis. In contrast to other proposals, SAR data is considered here to be the main input whereas AIS polls the supporting channel. The system kernel is built by the combination of three independent modules (coastline isolation, ship detection and ship classification) with two main purposes: to increase system independence and automatism. The former tries to limit the dependence on ancillary information (such as AIS), whereas the latter on human operator intervention. The three modules are integrated in a common framework developed with state-of-the-art web technologies. The result is a new concept for ship monitoring (including automatic SAR-based ship classification) that helps to better locate the error sources and reduce their dispersion. The system is able to ingest any type of SAR data for different modes and resolution, for instance ERS, ENVISAT, PALSAR, RADARSAT series or TerraSAR-X. Obviously, the performance would be strongly related with sensor features, but the system is designed to let single-polarimetric images with medium resolution provide reasonable results. This adds multi-sensor capability, which helps to reduce report refreshing time. In the paper, some examples will be processed and the main results analyzed. Preliminary tests for the ship classification module will be also presented, profiting from the ground-truth included within AIS-reports.
In the above paper [1], the funding information is incorrect. It should read as "This work was supported in part by the ECâCs FP7 NEREIDS project with ID 263468."
REFERENCES[1] G. Margarit and A. Tabasco, "Ship classification in single-Pol SAR images based on fuzzy logic,"
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