An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery

Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification.

[1]  B. Xu,et al.  Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image , 2003 .

[2]  Paul Scheunders,et al.  Statistical texture characterization from discrete wavelet representations , 1999, IEEE Trans. Image Process..

[3]  Thrasyvoulos N. Pappas,et al.  An Adaptive Clustering Algorithm For Image Segmentation , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[4]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[6]  DeLiang Wang,et al.  Extraction of hydrographic regions from remote sensing images using an oscillator network with weight adaptation , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  Lance M. Kaplan.  Extended fractal analysis for texture classification and segmentation , 1999, IEEE Trans. Image Process..

[8]  Peng Gong.  Reducing boundary effects in a kernel-based classifier , 1994 .

[9]  A. K. Milne,et al.  Segmentation of radar imagery using the Gaussian Markov random field model , 1999 .

[10]  B. S. Manjunath,et al.  Rotation-invariant texture classification using a complete space-frequency model , 1999, IEEE Trans. Image Process..

[11]  Yuttapong Rangsanseri,et al.  Synthetic aperture radar (SAR) image segmentation using a new modified fuzzy c-means algorithm , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[12]  Yunhan Dong,et al.  Segmentation and classification of vegetated areas using polarimetric SAR image data , 2001, IEEE Trans. Geosci. Remote. Sens..

[13]  Rama Chellappa,et al.  Unsupervised segmentation of polarimetric SAR data using the covariance matrix , 1992, IEEE Trans. Geosci. Remote. Sens..