Identification of SAR Detected Targets on Sea in Near Real Time Applications for Maritime Surveillance

Remote sensing technologies are widely used in maritime surveillance applications. Nowadays, spaceborne Synthetic Aperture Radar (SAR) systems provide outstanding capabilities for target detection at sea for large areas independently from the weather conditions. The generated value added target detection product is composed by complementary information from the Automatic Identification System (AIS). Resulting information layers provides a more reliable picture on the maritime situation awareness. This paper describes the approach of SAR-AIS data fusion and its visualization means developed for Near Real Time (NRT) Applications for Maritime Situational Awareness by the Maritime Security Lab at the Ground Station in Neustrelitz, part DLR’s German Remote Sensing Data Center (DFD). Presented implementation is based on combination of many open source geospatial libraries and frameworks (e.g., GDAL/OGR, Geoserver, PostgresSQL) and shows their effectiveness in the context of complex automated data processing in the frame of NRT requirements

Usability of Medium Resolution Optical Remote Sensing Images for Anomaly Detection in Maritime Surveillance Applications

As part of the project "Intelligent Assistance and Analysis Systems for Early Detection and Management of Maritime Hazardous Situations” (IntelliMar) an anomaly detection application was developed and validated based on the analysis of Automatic Identification System (AIS) and Earth Observation (EO) remote sensing data. For this task optical Earth observation medium resolution satellite data from Landsat-8 and Sentinel-2 were used and their suitability in the context of object detection was evaluated. In a two-step approach, deep-learning methods were used for object detection and classification, and the derived results were then applied to a set of anomaly rules for anomaly report generation and transmission

Sea State from High Resolution Satellite-borne Synthetic Aperture Radar Imagery

The Sea Sate Processor (SSP) was developed for fully automatic processing of high-resolution Synthetic Aperture Radar (SAR) data from TerraSAR-X (TS-X) satellites and implemented into the processing chain for Near Real Time (NRT) services in the DLR Ground Station "Neustrelitz". The NRT chain was organised and tested to provide the processed data to the German Weather Service (DWD) in order to validate the new coastal forecast model CWAM (Coastal WAve Model) in the German Bight of the North Sea with 900 m horizontal resolution. The NRT test-runs, wherein the processed TS-X data were transferred to DWD and then incorporated into forecast products reach the best performance about 10 min for delivery of processed TS-X data to DWD server after scene acquisition. To do this, a new empirical algorithm XWAVE_C (C = coastal) for estimation of significant wave height from X-band satellite-borne SAR data has been designed for coastal applications. The algorithm is based on the spectral analysis of subscenes and the empirical model function yields an estimation of integrated sea state parameters directly from SAR image spectra without transformation into wave spectra. To provide the raster coverage analysis, the SSP intends three steps of recognising and removing the influence of non-sea-state-produced signals in the Wadden Sea areas such as ships, buoys, dry sandbars as well as nonlinear SAR image distortions produced by e.g. short and breaking waves. For the validation, more than 150 TS-X StripMap scene sequences with a coverage of ~30 km × 300 km across the German Bight since 2013 were analysed and compared with in situ Buoy measurements from 6 different locations. On this basis, the SSP autonomous processing of TS-X Stripmap images has been confirmed to have a high accuracy with an error RMSE = 25 cm for the total significant wave height

Multiparametric Sea State from Spaceborne Synthetic Aperture Radar for Near Real Time Services

Spaceborne synthetic aperture radar (SAR) is a powerful tool for monitoring seas. The ability to work independently of sun illumination, cloud coverage and atmospheric conditions, as well as the capability of delivering spatial information, makes SAR one of the most perceptive instruments. The newest methods for processing SAR data with increased precision allow sea state fields to be estimated with local variabilities. For large areas in oceans where no in-situ measurements and only forecast predictions are available, this information is indispensable for global shipping and over human activity. Due to newest developments, the derived meteo-marine parameters can be transferred to weather services and to a ship’s bridge several minutes after acquisition, where the ship route can be optimized. The study presents a method and application for estimating series of integrated sea state parameters from satellite-borne SAR, allow processing of data from different satellites and modes in near real time (NRT). The developed Sea State Processor (SSP) estimates total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, mean wave period and period of wind sea. The algorithm was applied for the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WM) Level-1 (L1) products and also extended to the Xband TerraSAR-X (TSX) StripMap (SM) mode. The scenes are processed in raster and result in continuous sea state fields with the exception of S1 WV. Each 20 km × 20 km WV imagette, acquired every 100 km along the orbit, presents averaged values for each sea state parameter. The SSP was tuned and validated using two independent global wave models WAVEWATCH-3 (NOAA) and CMEMS (Copernicus) and NDBC buoys. The accuracy of Hs reaches an RMSE of 0.25 m by comparison with models (S1 WV); comparisons to NDBC worldwide buoys result into an RMSE of 0.3 m. Due to implemented parallelization, a fine rater step for scene processing can be practical applied: for example, S1 IW scene with coverage of 200 km × 250 km can be processed using raster step of 1 km (corresponds to ~50.000 subscenes) during minutes. The DLR Ground Station “Neustrelitz” applies SSP as part of a near real-time demonstrator service that involves a fully automated daily provision of surface wind and sea state parameters estimates from S1 IW for the North and Baltic Sea. All results and the presented methods are novel and provide a wide field for applications and implementations in prediction systems