Competitive neural-net-based system for the automatic detection of oceanic mesoscalar structures on AVHRR scenes

摘要

This paper shows a prototype automatic interpretation system for Advanced Very High Resolution Radiometer satellite ocean images. It is built on a three-level knowledge model (pixel, regional, and domain semantic problem levels) and uses several connectionist computational approaches. First, artificial neural net models (to the pixel level) were used for basic preprocessing tasks such as cloud masking. Next, a new connectionist technique using input vectors with nonnumerical regional marine features has also been developed and used in the identification phase. The paper shows some results of oceanic structure identification tasks (wakes, upwellings, and eddies) in infrared images of the northwest African coast and the Canary Islands. These results illustrate a procedure for improving automatic oceanic interpretation of satellite images.