Abstract
Automated classification of the signatures of atmospheric and oceanic processes in synthetic aperture radar (SAR) images of the ocean surface has been a difficult problem, partly because different processes can produce signatures that are very similar in appearance. For example, brightness fronts that are the signatures of horizontal wind shear caused by atmospheric processes that occur independently of properties of the ocean (WIN herein) often appear very similar to brightness fronts that are signatures of sea surface temperature (SST) fronts (SST herein). Using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived SST for validation, 302 SAR SST and 193 SAR WIN signatures were collected from over 250 RADARSAT-2 images of the Gulf Stream region using a Canny edge detector. A vector consisting of textural and contextual features was extracted from each signature and used to train and test logistic regression, maximum likelihood, and binary tree classifiers. Following methods proven effective in the analysis of SAR images of sea ice, textural features included those computed from the gray-level co-occurrence matrix for regions along and astride each signature. Contextual features consisted of summaries of the wind vector field near each signature. Results indicate that signatures labeled SST can be automatically discriminated from signatures labeled WIN using the mean wind direction with respect to a brightness front with an accuracy of between 80% and 90%.
