Cart (Loading....) | Create Account
Close category search window
 

Comparative algorithms for oil spill automatic detection using multimode RADARSAT-1 SAR data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Marghany, M. ; Inst. of Geospatial Sci. & Technol. (INSTeG), Univ. Teknol. Malaysia, Skudai, Malaysia ; Hashim, M.

This study is utilized comparative algorithms for automatic detection of oil spill from different RADARSAT-1 SAR mode data (Standard beam S2, Wide beam Wl and fine beam F1). In doing so, three algorithms are implemented: Co-occurrence textures; post supervised classification, and neural net work (NN). The study shows that the standard deviation of the estimated error for neural net work of value 0.12 is lower than Entropy and the Mahalanobis algorithms. In conclusion, ANN performed accurately as automatic detection tool for oil spill in RADARSAT data.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Date of Conference:

24-29 July 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.