Automatic detection of meddies through texture analysis of sea surface temperature maps

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

Authors

Colleges, School and Institutes

External organisations

  • Universidade Nova de Lisboa; Lisboa, Portugal
  • CENTRIA
  • Departamento de Informática

Abstract

A new machine learning approach is presented for automatic detection of Mediterranean water eddies from sea surface temperature maps of the Atlantic Ocean. A pre-processing step uses Laws' convolution kernels to reveal microstructural patterns of water temperature. Given a map point, a numerical vector containing information on local structural properties is generated. This vector is forwarded to a multi-layer perceptron classifier that is trained to recognise texture patterns generated by positive and negative instances of eddy structures. The proposed system achieves high recognition accuracy with fast and robust learning results over a range of different combinations of statistical measures of texture properties. Detection results are characterised by a very low rate of false positives. The latter is particularly important since meddies occupy only a small portion of SST map area.

Details

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2005
Event12th Portuguese Conference on Artificial Intelligence, EPIA 2005 - Progress in Artificial Intelligence - Covilha, Portugal
Duration: 5 Dec 20058 Dec 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3808 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference12th Portuguese Conference on Artificial Intelligence, EPIA 2005 - Progress in Artificial Intelligence
CountryPortugal
CityCovilha
Period5/12/058/12/05