Operationally-significant wind speed variability is often observed within synthetic aperture radar-derived wind speed (SDWS) images of the sea surface. This paper is meant as a first step towards automated distinguishing of meteorological phenomena responsible for such variability. In doing so, the research presented in this paper tests feature extraction and pixel aggregation techniques focused on mesoscale variability of SDWS. A sample of twenty eight SDWS images possessing varying degrees of near-surface wind speed variability were selected to serve as case studies. Gaussian high- and low-pass, local entropy, and local standard deviation filters performed well for the feature extraction portion of the research while principle component analysis of the filtered data performed well for the pixel aggregation. The findings suggest recommendations for future research.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering