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A Markov Random Field Model-based Fusion Approach to Segmentation of SAR and Optical Images

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3 Author(s)
Yi Yang ; Institute of Integrated Automation School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi Province, P. R. China ; Chongzhao Han ; Deqiang Han

In this paper, a data fusion approach to the segmentation of SAR and optical images in Markov random field (MRF) framework is proposed. In the joint segmentation scheme based on an MRF model defined on a region adjacency graph (RAG), a fusion rule made on local features of source images is developed for appropriately measuring the feature saliency and incorporating the source reliability of each data source to weigh the source influence in the segmentation procedure. A specific scheme for segmentation of a set of Landsat Thematic Mapper (TM) images and a synthetic aperture radar (SAR) image is presented in detail. Comparative analysis of the proposed segmentation approach against several conventional segmentation approaches carried out on synthetic and real datasets confirms the effectiveness of the proposed approach.

Published in:

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium  (Volume:4 )

Date of Conference:

7-11 July 2008