Skip to main content
padlock icon - secure page this page is secure

Investigation of genetic algorithms contribution to feature selection for oil spill detection

Buy Article:

$55.00 + tax (Refund Policy)

Oil spill detection methodologies traditionally use arbitrary selected quantitative and qualitative statistical features (e.g. area, perimeter, complexity) for classifying dark objects on SAR images to oil spills or look-alike phenomena. In our previous work genetic algorithms in synergy with neural networks were used to suggest the best feature combination maximizing the discrimination of oil spills and look-alike phenomena. In the present work, a detailed examination of robustness of the proposed combination of features is given. The method is unique, as it searches though a large number of combinations derived from the initial 25 features. The results show that a combination of 10 features yields the most accurate results. Based on a dataset consisting of 69 oil spills and 90 look-alikes, classification accuracies of 85.3% for oil spills and in 84.4% for look-alikes are achieved.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: Joint Research Centre (JRC) of the European Commission, Institute for the Protection and Security of the Citizen, Ispra, 21020 (VA), Italy 2: Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens, Heroon Polytechniou 9, Greece

Publication date: 01 January 2009

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more