SHIP DETECTION USING SENTINEL-1 SAR DATA
The Earth’s surface is covered with 72% water. This fact alone emphasizes the importance of proper monitoring and regulation of maritime activities. This monitoring can be useful in an array of applications including illegal transitions, rescue operations, territory regulation among many other applications. In order to achieve the task of “Maritime Surveillance” or simply the marine object detection, we need a structured approach combined with a set of algorithms. The objective of this paper is to study an emerging open source tool- Search for Unidentified Maritime Objects (SUMO) developed for the detection of ships which work regardless of weather conditions and coverage limits. Based on the Synthetic Aperture Radar (SAR) data, this paper aims to process the satellite-borne data provided by the Sentinel-1 satellite. Proposed by the Joint Research Centre, SUMO is a pixel-based algorithm which follows a structured approach in order to identify marine objects and remove false alarms. It is observed that many of the false alarms are caused due to the presence of land. These are reduced by using the buffered coastlines referred to as land masks. A local threshold is calculated using the background clutter for the generation of false alarm rate and the pixels above this threshold are identified and clustered to form targets. A reliability value is computed for the elimination of azimuth ambiguities. Also, various attributes of the detected targets are calculated in order to give an accurate description of ships and its characteristics. With the SAR data being freely available due to the open data policy of the EU’s Copernicus program, it has never been more viable to employ new methods for marine object detection and this paper explores this possibility by analyzing the results obtained. Specifically, the employed data consists of Sentinel-1 fine dual-pol acquisitions over the coastal regions of India.