Data fusion in a Markov random-field-based image segmentation approach

Paul C. Smits; Silvana G. Dellepiane

doi:10.1117/12.262877

17 December 1996 Data fusion in a Markov random-field-based image segmentation approach

Paul C. Smits, Silvana G. Dellepiane

Proceedings Volume 2955, Image and Signal Processing for Remote Sensing III; (1996) https://doi.org/10.1117/12.262877
Event: Satellite Remote Sensing III, 1996, Taormina, Italy

Abstract

Synthetic aperture radar data may contain useful information about small structures like rivers and man made structures. Using the Markov random field based segmentation algorithms, that perform quite well on homogeneously textured areas (i.e., agriculture land cover), these structures may be lost if they are small (1-2 pixel wide). The merging of various sources of information at a low level of the Markov random field region label process, makes it possible to recover at least partly the fine structures in the SAR data. The data fusion makes use of Bayesian inference about the Markovian property of neighborhood systems. This article shows that the proposed method is valid and technically feasible, based on extensive validations.

Citation Download Citation

Paul C. Smits and Silvana G. Dellepiane "Data fusion in a Markov random-field-based image segmentation approach", Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); https://doi.org/10.1117/12.262877

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