High-resolution Synthetic Aperture radar (SAR) images of coastal and off-coastal areas provide a nearly weather independent monitoring of small-scale oceanographic features. In this paper, we describe the advances, which have been achieved within the DTeddie project. Therefore, we present the application of formerly developed algorithms to high-resolution SAR data to estimate the sea surface current at sub-mesoscale. Inside the derived current sea surface current fields, vortical structures, e.g. eddies, will be detected by means of local current anomalies. Another approach uses the alignment of anchored ships as an indicator of the sea surface current. To infer dependent information for each ship, we present an automatic registration method between coastal photographs and the SAR images. Both approaches have proven to yield valuable results with respect to the detection of vortical current flows at sub-mesoscale.

Active contour methods like snakes, have become a basic tool in computer vision and image analysis over the last years. They have proven to be adequate for the task of finding boundary features like broken edges in an image. However, when applying the basic snake technique to synthetic aperture radar (SAR) remote sensing images, the detection of varying-contrast edges may not be satisfying. This is caused by the special imaging technique of SAR and the commonly known specklenoise. In this paper we propose the use of asymmetric external energy terms to cope with this problem. We show first results of the method for the detection of edges of tidal creeks using an ENVISAT ASAR image. These creeks can be found in the World Heritage Site ”Wadden Sea” located at the German Bight (North Sea).

The Integral Equation Model (IEM) predicts the normalized radar cross section (NRCS) of dielectric surfaces given surface and radar parameters. To derive the surface parameters from the NRCS using the IEM, the model needs to be inverted. We present a fast method of this model inversion to derive soil surface roughness parameters from synthetic aperture radar (SAR) remote sensing data. The model inversion is based on two different collocated SAR images of different bands, the derivation of the parameters cannot be done using one band alone. The computation of the model and the model inversion are very time consuming tasks and therefore may be impractical for large remote sensing data. We present an approach that is based on a few model assumptions to speed up the computation of the surface parameters. We applied the algorithm to detect the correlation length of the surface for dry-fallen areas in the World Cultural Heritage ”Wadden Sea”, a coastal tidal flat at the German Bight (North Sea). The results are very promising and may be used for a classification of the area in future steps.

In [6] we presented an approach to measure sea surface currents from satellite images by computing the displacement vectors of distinctive image features or measuring the optical flow between successive images of the same scene. These algorithms are based on the assumption that the image flow observed is caused by the motion of surface films with the currents to be measured. Optical flow due to other image features, e.g. ship wakes, does not correspond to the motion field and causes systematic errors. The decision whether a derived measurement is valid or not by domain experts is very time-consuming. We present first steps towards an automatic knowledge-based approach that uses description logic to validate the measurements of sea surface currents. The terminological knowledge is based on the expert's knowledge of the domain in conjunction with a geographical database containing factual knowledge about the scene. The combination of different sources of knowledge makes it possible to infer about the validity of sea surface current measurements from image data by reasoning based on the local context of the image features.

Sequential multi-sensor satellite images are used for the computation of mesoscale surface currents in the Northern and Southern Baltic Proper. Different marine surface films and accumulated algae at the water surface are imaged by the sensors working in the optical, infrared, and microwave part of the electromagnetic spectrum and can thus be used as tracers for the local motion of the sea surface. Due to the generality of the problem, there are different algorithms available for motion estimation using object tracking in images. To apply these to the field of sea surface current estimation, we need to take the smoothness assumptions into account that the algorithms were built up on. We present the influence of different spatial constraints in the algorithms for the derivation of mesoscale sea surface currents using multi-sensor / multi-channel satellite images by means of a quantitative comparison with model results provided by the German Federal and Maritime Agency (BSH).

We used multi-sensor satellite images to compute small-scale surface currents in the Northern Baltic Sea. The images were acquired by the Thematic Mapper (TM) aboard Landsat, by the Synthetic Aperture Radar (SAR) aboard ERS-2, and by the Wide-Field Scanner (WiFS) aboard IRS-1C within two hours on 15 July, 1997, over the Northern Baltic Proper. Most of them show manifestations of an ongoing algae bloom, which are driven by the local currents. Pairs of images acquired at either different electromagnetic frequencies (TM and SAR) or at the same electromagnetic bands (TM and WiFS) are used for applying cross-correlation or partial differentiation techniques, respectively. Our results are insofar promising as the computed two-dimensional surface current fields complement existing data from numerical models. However, limitations of the proposed method are due to the strong dependence of the visibility of algae bloom manifestations on local weather conditions and to the low availability of satellite data.