The satellite of the TerraSAR-X mission, called TSX, was launched on 15 June 2007 and its identically constructed twin satellite TDX, which is required by the mission TanDEM-X, launched on 21 June 2010. Together they supply high-quality radar data in order to serve two mission goals: Scientific observation of Earth and the provisioning of remote sensing data for the commercial market (TerraSAR-X mission) and the generation of a global digital elevation model (DEM) of Earth’s surface (TanDEM-X mission). On the occasion of the 10th anniversary of the mission, the focus will be on the development of the TerraSAR-X system during this period, including the extension of the ground segment, the evolution of the product portfolio, dedicated mission campaigns, radar experiments, refinement of the satellite operations and orbit control, and the results of the performance monitoring. Despite numerous interventions in the overall system, we managed to incorporate new scientific and commercial requirements and to improve and enhance the overall system in order to fulfill the increasing demand for Earth observation data without noticeable interruptions to ongoing operations.

Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output values are 4 ice classes of Stage of Development and Ice Free. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can therefore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satellite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the German Aerospace Center (DLR) ground station in Neustrelitz.

In the context of the project real-time services for maritime security (Echtzeitdienste für die Maritime Sicherheit—security), an experimental research platform for validation of maritime products derived from remote sensing data, was developed. This article describes the work carried out to derive ship-, wind-, and wave detection products out of Sentinel-1 remote-sensing data by DLR’s Maritime Safety and Security Lab in Neustrelitz, part of the German Remote Data Center DFD. The activity aims to the fulfilment of project requirements, primarily to support the need for near real-time performance up to 15 min, as those in maritime situational awareness. The development and implementation cover the task of level 0 processing, based on DLR’s front end processor, the implementation of the framework for real-time processing up to level 2 (value adding), as well as the development of a hardware-independent virtual-processing platform.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Nowadays, maritime security faces many kinds of problems – environmental hazards, unlawful actions such like piracy, cargo theft, illegal border crossing etc. These challenges bring the situational awareness of this domain to a high level of importance. Optical satellite images, captured during cloudless weather conditions, are valuable source of information about situation at sea. On the one hand, modern very high resolution (VHR) optical sensors (e.g. WorldView family) taking images with spatial resolution higher than 0.5 m per pixel, enabling to perform object (ship) detection tasks. On the other hand, high resolution (HR) sensors like Landsat-8 and Sentinel-2A(B) are able to cover relatively large areas and are suitable for environment monitoring tasks. Developed at the German Remote Sensing Data Center (DFD), part of the German Aerospace Center (DLR), Maritime Security Lab Processing Framework is intended to support the operational maritime surveillance near real time (NRT) services based HR and VHR optical satellite data. The Framework supports automated request driven processing from different satellite missions provided by a network of different ground stations and service providers. Actionable information products are created in an automatic processing chain including image pre-processing, data transcription and GUI based interactive value adding and validation. The paper will focus on the overall architecture of the framework including workflow of data handling, the interfaces and components, needed to enable fast data access for operator analysis and supervision.

In the context of the project OpSSERVE - Optical Satellite Services for EMSA (European Maritime Safety Agency) – the European Space Imaging (EUSI) and the German Aerospace Center (DLR) established for the first time a fully operational and near real-time service to detect vessels and maritime activities with optical satellite imagery . The service was implemented in 2013 and contribute to maritime situation awareness, e.g. in order to reduce the risk of maritime accidents, marine pollution and the loss of human life at sea. Since its activation in 2007, EMSA’s CleanSeaNet (CSN) system provides pollution detection services in support of maritime surveillance and decision making for all participating European member states based on SAR satellite data. The main advantage of SAR data is their independence of weather conditions (cloud coverage), but the identification of small vessels is rather difficult. The use of very high resolution (VHR) optical data provides valuable information to identify small vessels. From the VHR optical imagery, actionable information products are created in an automatic processing chain including image pre-processing, data transcription, automatic vessel detection, GUI based interactive vessel and activity detection and finally the delivery of standardized products to EMSA. The service provides access to globally collected satellite imagery and receives data and derived products in near real-time (1 or 3 hours). In OpSSERVE, five satellite missions are available: WorldView-1, WorldView-2, GeoEye-1, IKONOS and EROS-B. The poster will present the experiences of the operational service and give an overview on future aspects and possible applications.