Synthetic aperture radar image segmentation by a detail preserving Markov random field approach

A multichannel image segmentation method is imposed that utilizes Markov random fields (MRFs) with adaptive neighborhood (AN) systems. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighborhood system is achieved by following a criterion that makes use of the Markovian property exploiting the local image content. The MRF segmentation approach with AN systems (MRF-AN) makes it possible to better preserve small features and border areas. The purpose of the paper is to show the usefulness of the concept of MRF-AN for SAR image segmentation.

[1]  David B. Cooper,et al.  Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Anil K. Jain,et al.  Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images , 1994, IEEE Trans. Geosci. Remote. Sens..

[3]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Silvana G. Dellepiane,et al.  Information fusion in a Markov random field-based image segmentation approach using adaptive neighbourhoods , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Rama Chellappa,et al.  Segmentation of polarimetric synthetic aperture radar data , 1992, IEEE Trans. Image Process..

[6]  S. Stehman Estimating the Kappa Coefficient and its Variance under Stratified Random Sampling , 1996 .

[7]  J. Villasenor,et al.  On the use of multi-frequency and polarimetric radar backscatter features for classification of agricultural crops , 1994 .

[8]  Silvana G. Dellepiane,et al.  Data fusion in a Markov random-field-based image segmentation approach , 1996, Remote Sensing.

[9]  Jon Sticklen,et al.  Knowledge-based segmentation of Landsat images , 1991, IEEE Trans. Geosci. Remote. Sens..

[10]  Feng Chen,et al.  Iterative segmentation algorithms using morphological operations , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Patrick Pérez,et al.  Restriction of a Markov random field on a graph and multiresolution statistical image modeling , 1996, IEEE Trans. Inf. Theory.

[12]  Robert M. Haralick,et al.  A methodology for quantitative performance evaluation of detection algorithms , 1995, IEEE Trans. Image Process..

[13]  Sebastiano B. Serpico,et al.  Markov random field based image segmentation with adaptive neighborhoods to the detection of fine structures in SAR data , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[14]  Vittorio Murino,et al.  Distributed propagation of a-priori constraints in a Bayesian network of Markov random fields , 1993 .

[15]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[16]  C. A. Murthy,et al.  Analysis of IRS imagery for detecting man-made objects with a multivalued recognition system , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[18]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Rama Chellappa,et al.  Stochastic and deterministic networks for texture segmentation , 1990, IEEE Trans. Acoust. Speech Signal Process..

[20]  Georgy L. Gimel'farb,et al.  Probabilistic models of digital region maps based on Markov random fields with short- and long-range interaction , 1993, Pattern Recognit. Lett..

[21]  James W. Modestino,et al.  A Markov Random Field Model-Based Approach To Image Interpretation , 1989, Other Conferences.

[22]  Gianni Vernazza,et al.  A multilevel GMRF-based approach to image segmentation and restoration , 1993, Signal Process..

[23]  C. S. Regazzoni,et al.  Global Probabilistic Reinforcement of Straight Segments , 1994 .

[24]  Patrick A. Kelly,et al.  Adaptive segmentation of speckled images using a hierarchical random field model , 1988, IEEE Trans. Acoust. Speech Signal Process..

[25]  Donald Geman,et al.  An Active Testing Model for Tracking Roads in Satellite Images , 1996, IEEE Trans. Pattern Anal. Mach. Intell..