## 1 Introduction  
This paper presents a Ship Monitoring System (SIMONS) that combines the information inferred from Synthetic Aperture Radar (SAR) data [1] with transponder-based polls [such as the Automatic Identification System (AIS) from the International Maritime Organization (IMO) [2]]. In contrast to other monitoring approaches, SAR images are the main channel whereas AIS reports are the complementary one. SIMONS has been developed in the framework of the MARISS project [3]. This project is part of the series of projects (IMPAST, LIMES,...) promoted by the European Commission (EC) to develop efficient and reliable ship monitoring. The main goal is to provide authorities with a system that controls all the activities of ships and permits them to verify law compliance. Other benefits would be to protect the ecosystem, to provide surveillance along the transportation corridors, to monitor illegal immigration and to assure sustainable economic development.  
The first operational services were developed in the early nineties and rely on the usage of active on-board transponders. There, GPS-based receivers provide real-time ship status to monitoring centers via satellite communications. Such systems have proven to be very accurate for supporting decision making but present an intrinsic limitation, which is related to the transponder itself. Certainly, ships are located if these devices are active, in such a way that any (accidental or intended) malfunction prevents the system from tracking them. Such lack of independence is not desirable for a service intended to support law enforcement. More reliable solutions should be developed and SAR can be essential in this situation.  
SAR is a microwave coherent remote sensing technology able to provide 2D reflectivity images of large scenes with fine resolutions and 24-hour all-weather sensing capability. It overcomes the lack of independence of transponders, but needs image analysis and post-processing techniques to infer all the required information. This implies the usage of models and parametric solutions that are affected by limiting factors. The most important ones are image resolution, speckle noise and image distortions due to the dynamic environment [1], which add uncertainty to the final reports. Currently, SAR has been partially exploited in the field of ship monitoring, tackling minor issues such as the detection of large ships. No information regarding ships categories, coastline detection, the detection of small ships and the confidence on the detected spots is available yet under an operational basis.  
The monitoring system presented in this paper tries to overcome all of these limitations so that the gap in ship monitoring could be filled. It uses a state-of-the-art SAR Ship Detection and Classification Algorithm (SIDECAR), jointly with an advanced web interface to disseminate the results. The main goals are to drop the dependence on transponders, to provide a friendly and secure environment, to infer a reliable and complete set of information layers and to design a set of tools to support decision making. Recent feedback with users has confirmed that all of these goals have been achieved.  
In this manuscript, the main elements of SIMONS are described, jointly with some processing examples applied to ENVISAT and ERS data. Cross-check of the results with ground-truth AIS-polls is also included to show ship detection and classification performance. Note, however, that the paper does not cover the inherent problem of SAR related to the sparseness of the overpasses. It is assumed that SIMONS is embedded in a monitoring service where this issue is solved. In real applications, a proper solution could rely on a multi-sensor approach or an always-on station airborne SAR.