Title :
A Markov Random Field Model-based Fusion Approach to Segmentation of SAR and Optical Images
Author :
Yang, Yi ; Han, Chongzhao ; Han, Deqiang
Author_Institution :
Inst. of Integrated Autom. Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., Xi´´an
Abstract :
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.
Keywords :
Markov processes; geophysical signal processing; image fusion; image segmentation; remote sensing by radar; synthetic aperture radar; Landsat Thematic Mapper imagery; Markov random field model; SAR; feature saliency; image fusion; image segmentation; optical images; region adjacency graph; source reliability; synthetic aperture radar; Adaptive optics; Bayesian methods; Image segmentation; Integrated optics; Markov random fields; Optical sensors; Remote sensing; Satellites; Sensor phenomena and characterization; Synthetic aperture radar; Markov random field (MRF); data fusion; image segmentation; remote sensing; synthetic aperture radar (SAR);
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4779844
