Abstract
A new method is proposed to extract urban areas from SAR imagery using two different Gaussian Markov Random Field (GMRF) models. Firstly, by making an initial segmentation by a watershed algorithm, we adopt a particular GMRF model proposed by Descombes et al. (the model is called RGMRF model, distinguished from the conventional GMRF model) to acquire urban areas. In the first model a part of the urban areas from the SAR image is extracted with some missing detection. Then, taking the first result as a training sample, we use the conventional GMRF model to redo the extraction. In the second model a larger area is detected including all urban areas with some false detection. Finally, we fuse the two results using a region-growing algorithm to form the final detected urban area. Experimental results show that the proposed method can obtain accurate urban areas delineation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
X. Descombes and M. Sigelle, “Estimating Gaussian Markov Random Field Parameters in Nonstationary Framework Application to Remote Sensing Imaging,” IEEE Trans. Image Process. 8(4), 490–503 (1999).
R. Chellappa, S. S. Chatterjee, and R. Bagdazian, “Texture Synthesis and Compression, using Gaussian-Markov Random Field Models,” IEEE Transactions on Systems, Man and Cybernetics, 15, 298–303 (1985).
Y. Dong, B. C. Forster, and A. K. Milne, “Segmentation of Radar Imagery using Gaussian Markov Random Field Models and Wavelet Transform Techniques,” in Proceedings of IGARSS 97, Singapore, 1997, Vol. 4, pp. 2054–2056.
M. Grimaud, “A New Measure of Contrast: Dynamics,” in Proc. SPIE Image Algebra and Morphological Processing III, San Diego, 1992, Vol. SPIE 1769, pp. 292–305.
D. K. Panjwani and G. Healey, “Markov Random Field Models for Unsupervised Segmentation of Textured Color Images,” IEEE Trans. Pattern Anal. Machine Intell. 17, 939–954 (1995).
Y. Dong, B. C. Forster, and A. K. Milne, “Segmentation of Radar Imagery using Gaussian Markov Random Field Model,” Int. J. Remote Sensing 20(8), 1617–1639 (1999).
Author information
Authors and Affiliations
Additional information
The text was submitted by the authors in English.
Rights and permissions
About this article
Cite this article
Yang, Y., Sun, H. & Cao, Y. Unsupervised urban area extraction from SAR imagery using GMRF. Pattern Recognit. Image Anal. 16, 116–119 (2006). https://doi.org/10.1134/S1054661806010378
Received:
Issue Date:
DOI: https://doi.org/10.1134/S1054661806010378