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

Improving PolSAR Land Cover Classification With Radiometric Correction of the Coherency Matrix

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

3 Author(s)
Atwood, D.K. ; Geophys. Inst., Univ. of Alaska Fairbanks, Fairbanks, AK, USA ; Small, D. ; Gens, R.

The brightness of a SAR image is affected by topography due to varying projection between ground and image coordinates. For polarimetric SAR (PolSAR) imagery being used for purposes of land cover classification, this radiometric variability is shown to affect the outcome of a Wishart unsupervised classification in areas of moderate topography. The intent of this paper is to investigate the impact of applying a radiometric correction to the PolSAR coherency matrix for a region of boreal forest in interior Alaska. The gamma naught radiometric correction estimates the local illuminated area at each grid point in the radar geometry. Then, each element of the coherency matrix is divided by the local area to produce a polarimetric product that is radiometrically “flat.” This paper follows two paths, one with and one without radiometric correction, to investigate the impact upon classification accuracy. Using a Landsat-derived land cover reference, the radiometric correction is shown to bring about significant qualitative and quantitative improvements in the land cover map. Confusion matrix analysis confirms the accuracy for most classes and shows a 15% improvement in the classification of the deciduous forest class.

Published in:

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:5 ,  Issue: 3 )

Date of Publication:

June 2012

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.