Unsupervised change detection based on improved Markov random field technique using multichannel synthetic aperture radar images

Sara Salehi; Mohammad Javad Valadan Zoej

doi:10.1117/1.JRS.8.083591

15 July 2014 Unsupervised change detection based on improved Markov random field technique using multichannel synthetic aperture radar images

Sara Salehi, Mohammad Javad Valadan Zoej

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083591 (July 2014). https://doi.org/10.1117/1.JRS.8.083591

Abstract

Change detection represents an important remote sensing tool in environmental monitoring and disaster management. In this respect, multichannel synthetic aperture radar (SAR) data offer great potential because of their insensitivity to atmospheric and sun-illumination conditions (over optical multispectral data) and the improved discrimination capability they may provide compared to single-channel SAR. The problem of detecting the changes caused by flooding is addressed by a contextual unsupervised technique based on a Markovian data fusion approach. However, the isotropic formulation of Markov random field (MRF) models causes oversmoothing of spatial boundaries in the final change maps. In order to reduce this drawback, an edge-preserving MRF model is proposed and formulated by using energy functions that combine the edge information extracted from the produced edge maps using competitive fuzzy rules and Canny technique, the information conveyed by each SAR channel, and the spatial contextual information. The proposed technique is experimentally validated with semisimulated data and real ASAR-ENVISAT images. Change detection results obtained by the improved MRF model exhibited a higher accuracy than its predecessors for both semisimulated (average 12%) and real (average 6%) data.

Citation Download Citation

Sara Salehi and Mohammad Javad Valadan Zoej "Unsupervised change detection based on improved Markov random field technique using multichannel synthetic aperture radar images," Journal of Applied Remote Sensing 8(1), 083591 (15 July 2014). https://doi.org/10.1117/1.JRS.8.083591

Published: 15 July 2014

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