Unsupervised urban area extraction from SAR imagery using GMRF

Authors:
Y. Yang;H. Sun;Y. F. Cao
Affiliations:
School of Electronic Information, Wuhan University, Wuhan, China 430079;School of Electronic Information, Wuhan University, Wuhan, China 430079;School of Electronic Information, Wuhan University, Wuhan, China 430079
Venue:
Pattern Recognition and Image Analysis
Year:
2008

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Abstract

This paper presents a new method for unsupervised urban area extraction from SAR imagery using two different GMRF models. One model is the T-based GMRF model proposed by Xavier Descombes specially for acquiring urban area in panchromatic SPOT imagery. When it is used for urban area extraction from SAR imagery, some missing detection occurs. The other model is the conventional GMRF model that requires training samples for urban area extraction. When it is used for SAR imagery, the extraction result includes all urban areas and some false detection. Three steps are made up in our method. First, we adopt a threshold for the T-based GMRF model parameter T to acquire the result of urban area extraction. Then, taking the result as training samples, we estimate the conventional GMRF model parameters and acquire a new result of urban area extraction. Finally, we fuse the two results above using a region-growing algorithm to form the final accurate urban area extraction. Experimental results show that the proposed unsupervised approach can obtain accurate urban area delineation.