Analysis of Coastal Areas Using SAR Images: A Case Study of the Dutch Wadden Sea Region

The increased availability of civil SAR (Synthetic Aperture Radar) satellite images with different resolution allows us to compare the imaging capabilities of these instruments, to assess the quality of the available data, and to investigate different areas (e.g., the Wadden Sea region). In our investigation, we propose to explore the content of TerraSAR-X and Sentinel-1A satellite images via a data mining approach in which the main steps are patch tiling, feature extraction, classification, semantic annotation, and visual-statistical analytics. Once all the extracted categories are mapped and quantified, then the next step is to interpret them from an environmental point of view. The objective of our study is the application of semi-automated SAR image interpretation. Its novelty is the automated multi-class categorisation of coastal areas. We found out that the north-west of the Netherlands can be interpreted routinely as land surfaces by our satellite image analyses, while for the Wadden Sea we can discriminate the different water levels and their impact on the visibility of the tidal flats. This necessitates a selection of time series data spanning a full tidal cycle.

[1]  Henri Maitre,et al.  Processing of Synthetic Aperture Radar (SAR) Images , 2008 .

[2]  Andrey Pleskachevsky,et al.  WATERLINE DETECTION AND MONITORING IN THE GERMAN WADDEN SEA USING HIGH RESOLUTION SATELLITE-BASED RADAR MEASUREMENTS , 2015 .

[3]  S. Lehner,et al.  Monitoring river estuaries and coastal areas using TerraSAR-X , 2009, OCEANS 2009-EUROPE.

[4]  J. Thissen,et al.  General introduction to the lists of threatened biotopes, flora and fauna of the trilateral Wadden Sea Area (red data book) , 1996, Helgoländer Meeresuntersuchungen.

[5]  K. Dijkema,et al.  Salt Marshes in the Netherlands Wadden Sea: Rising High-Tide Levels and Accretion Enhancement , 1990 .

[6]  Mihai Datcu,et al.  Categorization based Relevance Feedback Search Engine for Earth Observation Images Repositories , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[7]  Shiyong Cui,et al.  The Earth Observation Image Librarian (EOLIB): The Data Mining Component of the TerraSAR-X Payload Ground Segment , 2016 .

[8]  J. Thissen,et al.  Red lists of biotopes, flora and fauna of the trilateral Wadden Sea area, 1995 , 1996 .

[9]  Georg Heygster,et al.  Topographic Mapping of the German Tidal Flats Analyzing SAR Images With the Waterline Method , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Martin Gade,et al.  The use of high-resolution Radarsat-2 and Terrasar-X imagery to monitor dry-fallen intertidal flats , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[11]  Shiyong Cui,et al.  Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Shiyong Cui,et al.  Improved image classification by proper patch size selection: TerraSAR-X vs. Sentinel-1A , 2016, 2016 International Conference on Systems, Signals and Image Processing (IWSSIP).

[13]  Mihai Datcu,et al.  Land Cover Semantic Annotation Derived from High-Resolution SAR Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  S. Quegan,et al.  Understanding Synthetic Aperture Radar Images , 1998 .

[15]  E. Baltsavias,et al.  Automatic Extraction of Man-Made Objects from Aerial and Space Images (II) , 1995 .

[16]  W. Wolff Ecology of the Wadden Sea , 1983 .

[17]  W. Wolff,et al.  Expected Effects of Climatic Change on Marine Coastal Ecosystems , 1990, Developments in Hydrobiology.

[18]  Stefan Wiehle,et al.  Automated Waterline Detection in the Wadden Sea Using High-Resolution TerraSAR-X Images , 2015, J. Sensors.