Mapping mesoscale phenomena in SAR images

We have been able to detect and to classify five atmospheric phenomena in SAR images: low wind areas, katabatic wind, island wakes, atmospheric gravity waves and atmospheric convective cells. This is like classifying the sea surface into existing phenomena with the same logic of classifying land into existing land categories. An object-based environment was used to identify the phenomena and fuzzy logic used to classify them. Several features extracted for each phenomenon were used for feeding of the classifier. The method developed used 16 SAR images and accuracy was examined in another 16 SAR images using 457 object samples. The method proved to be successful with an overall accuracy of 80% and Kappa index 0.75. The class of low wind areas negatively affected the overall accuracy due to overlapping feature values with the rest categories.

Radar satellite observations above sea are dedicated to small scale and mesoscale phenomena in the ocean and the marine boundary layer. What the Synthetic Aperture Radar (SAR) sensors see on the ocean surface is primarily the variation of the Bragg resonant waves. SAR images contain information on the processes in the atmosphere – sea surface layer, in particular when variation of wind speed and direction is significant. The sea roughness modulation of the radar backscatter cross section reveals manifestations of dynamic upper sea and atmospheric boundary layer structures. The dominant processes associated with such dynamic features are closely connected with the nature of each phenomenon. Several publications exist in the literature modeling and explaining each phenomenon separately. But only a few aim at the identification of each phenomenon in an automatic or semi-automatic way.