Morphological approach to multiband synthetic aperture radar (SAR) image segmentation

Silvina J. Loccisano; Marta Mejail; Julio Cesar Jacobo Berlles

doi:10.1117/12.253446

8 October 1996 Morphological approach to multiband synthetic aperture radar (SAR) image segmentation

Silvina J. Loccisano, Marta Mejail, Julio Cesar Jacobo Berlles

Proceedings Volume 2823, Statistical and Stochastic Methods for Image Processing; (1996) https://doi.org/10.1117/12.253446
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

The present work is a report of the results obtained using M-estimators, Mahalanobis distance and openings and closings in supervised segmentation of multiband SAR images. Once the training zones on the SAR images are established, the M- estimators robust estimation method is used to determine the mean value and the covariance matrix for each segment. Then, to carry out the first stage in the segmentation process, the Mahalanobis distance is used. To improve the segmentation obtained in the previous stage, a sequence of openings and closing s with a structuring element of growing size is applied. Real and synthetic images were used to evaluate the results.

Citation Download Citation

Silvina J. Loccisano, Marta Mejail, and Julio Cesar Jacobo Berlles "Morphological approach to multiband synthetic aperture radar (SAR) image segmentation", Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); https://doi.org/10.1117/12.253446

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available