科研之友
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摘要
Satellite radar 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 providing signatures of local wind changes and imprints of atmospheric phenomena, and the areas of enhanced wave breaking associated with wave-current interaction for oceanographic processes like currents, fronts and eddies. SAR images contain information about the processes in the marine-atmosphere boundary layer (MABL), in particular when variation of wind speed and direction is significant. Several publications are available in the literature modeling and explaining each phenomenon separately. However, only a few focus on the identification of each phenomenon in an automatic or semi-automatic way. The objective of the present paper is 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. 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 the classifier. The proposed method 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 of 0.75. The class of low wind areas negatively affected the overall accuracy due to overlapping feature values with the rest of the categories.
- 出版日期2015-4
