A neural network approach for remote detection of marine eddies

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents a machine learning approach for detection of Mediterranean water eddies from sea surface temperature maps of the Atlantic Ocean. Two methods based on texture analysis of the satellite imagery are evaluated. Given a map point, the first method extracts information on the surrounding thermal gradient and arranges it as a numerical vector of gradient angles. The second method uses Laws' algorithm to create a vector of numerical measures of structural features. In both the cases, a neural network is trained to recognise those numerical patterns that reveal the presence of eddy structures. Both the algorithms achieve high recognition accuracy and fast and robust learning results. Particularly important are the very low rates of false detections obtained, since eddies occupy only a small portion of the ocean area. Compared to Laws' method, the gradient-based algorithm gives comparable recognition accuracies with a lower design effort and at reduced computational costs. The simple and modular structure of the gradient-based method also compares favorably to the complexity other algorithms for identification of marine phenomena published in the literature. Given the competitive accuracy results obtained, the gradient-based approach may be preferable to the currently employed techniques since it is simpler and more easily reconfigurable.

Original languageEnglish
Title of host publicationOCEANS 2006 - Asia Pacific
Publication statusPublished - 2007
EventOCEANS 2006 - Asia Pacific - , Singapore
Duration: 16 May 200719 May 2007


ConferenceOCEANS 2006 - Asia Pacific

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Ocean Engineering


Dive into the research topics of 'A neural network approach for remote detection of marine eddies'. Together they form a unique fingerprint.

Cite this