Anja Frost

A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images

We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.

Automated Iceberg Detection Using High-Resolution X-Band SAR Images

Abstract. In northern latitudes, icebergs frequently cross shipping routes and impair marine traffic. To improve ship routing, we explore the capabilities of an algorithm that detects and charts icebergs from images provided by the German radar satellite TerraSAR-X. TerraSAR-X is in a near-polar orbit, equipped with an active X-Band radar antenna and, thus, allows monitoring the ocean and frozen waters regardless of cloud cover and darkness. The algorithm we apply is based on the iterative censoring constant false alarm rate (IC-CFAR) detector, which has proven its usefulness for terrestrial target detection already. Unlike the standard approach, we not only estimate statistical properties of open water intensities expressed by a probability density function, but also search for recurring patterns (i.e., waves). This allows discriminating icebergs from most false alarms that arise from rough sea and strong winds. Experiments carried out with a series of HH-polarized TerraSAR-X Stripmap images acquired between 2012 and 2015 confirm that, due to consideration of wave pattern during image processing, the false alarm rate is reduced by a factor of 3.

Comparing automated sea ice classification on single-pol and dual-pol TerraSAR-X data

We compare classification of sea ice based on TerraSAR-X (TS-X) images for single-polarization and dual-polarization imaging modes. A texture based implementation for neural network classification on single-polarized ScanSAR data is presented. Likewise we propose an approach for operational generation of dual-polarized Stripmap data (with a different neural network architecture). Polarimetric feature quality in terms of information content is discussed for the latter implementation. Based on these results, neural network classification is applied to image acquired over Svalbard, Baffin Bay, and the Barents Sea. Our successful results justify to increase efforts into exploring further application potential of a software suite which comprises both algorithms. Such a tool may then provide navigational assistance for maritime users in near-real time.

First Tests on Near Real Time Ice Type Classification in Antarctica

In this paper, we explore the capabilities of an algorithm for ice type classification. Our main motivation and exemplary application was the recent incident of the research vessel Akademik Shokalskiy, which was trapped in pack ice for about two weeks. Strong winds had driven ice floes into a bay, forming an area of pack ice, blocking the ships advancement. High-resolution satellite images helped to assess the ice conditions at the location. To extract relevant information automatically from the images, we apply an algorithm that is aimed to generate an ice chart, outlining the different ice type zones such as pack ice, fast ice, open water. The algorithm is based on texture analysis. Textures are selected that allow recognition of different structures in ice. Subsequently, a neural network performs the classification. Since results are output in near real time, the algorithm offers new opportunities for ship routing in ice infested areas.

SAR-based wind fields over offshore wind farms — A valuable tool for planning, monitoring and optimization

The number of offshore wind facilities is increasing with a proportionate decline in fossil and nuclear power production. The study of turbulent wakes inside a turbine cluster is a very important topic in order to optimize cluster layout for power production. With an increasing density of wind farms in the exclusive economic zone (EEZ) of a country, shadowing effects of wind farms on adjacent clusters are becoming an important issue for wind farm performance and need to be investigated to improve power harvest predictions. We present a comparative study of wind fields of different resolutions and coverages derived from TerraSAR-X and Sentinel-1 images. We elucidate the benefits of certain data for particular applications.

High resolution sea ice drift estimation using combined TerraSAR-X and RADARSAT-2 data: First tests

High resolution sea ice drift fields, the location and extend of converging and diverging zones as well as ice ridges are most important parameters for ship navigation in ice infested waters. In this paper, we present the prototype of a new processor which is aimed to derive the surface ice parameters on the basis of pairs of space-borne Synthetic Aperture Radar (SAR) data of the same and of different sensors, i.e. from data of different bands, resolutions, and orbits. The study is carried out on image data collected during a joint campaign with the Office of Naval Research (ONR) in the western Arctic in 2015. The algorithm proposed is foreseen to be integrated into near-real time (NRT) processing chain at DLR ground stations in order to provide time-critical information as soon as possible to users and stakeholders.