On the accurate detection and enhancement of oceanic features observed in satellite data

Abstract Various methods to accurately and efficiently compute gradients, detect edges, and enhance features in satellite data are presented. Error rate criteria and spatial localization criteria are used to statistically evaluate the performance of these methods for both noise-free and noise-contaminated images. Results from the tests using noise-contaminated images can be used to infer the performance of these operators on AVHRR images with partially undetected clouds. Operators most suited to remotely-sensed applications (e.g., support of real-time experiments at sea, image classification studies, and updating numerical models of ocean - atmosphere circulation) are identified, and a set of practical considerations for accurate gradient and/or edge detection in remotely-sensed data is given.

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