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Texture Analysis through a Markovian Modelling and Fuzzy Classification: Application to Urban Area Extraction from Satellite Images

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Abstract

Herein we propose a complete procedure to analyze and classify the texture of an image. We apply this scheme to solve a specific image processing problem: urban areas detection in satellite images. First we propose to analyze the texture through the modelling of the luminance field with eight different chain-based models. We then derived a texture parameter from these models. The effect of the lattice anisotropy is corrected by a renormalization group technique coming from statistical physics. This parameter, which takes into account local conditional variances of the image, is compared to classical methods of texture analysis. Afterwards we develop a modified fuzzy Cmeans algorithm that includes an entropy term. The advantage of such an algorithm is that the number of classes does not need to be known a priori. Besides this algorithm provides us with further information, i.e. the probability that a given pixel belongs to a given cluster. Finally we introduce this information in a Markovian model of segmentation. Some results on SPOT5 simulated images, SPOT3 images and ERS1 radar images are presented. These images are provided by the French National Space Agency (CNES) and the European Space Agency (ESA).

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References

  • Akaike, H. 1974. A new look at the statistical model identification. IEEE Trans. on Automatic Control, 19:716–723.

    Google Scholar 

  • Baraldi, A. and Parmiggiani, F. 1990. Urban area classification by multispectral spot images. IEEE Trans. on Geoscience and Remote Sensing, 28(4):674–680.

    Google Scholar 

  • Besag, J. 1986. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B, 48:259–302.

    Google Scholar 

  • Besag, J.E. 1974. Spatial interaction and the statistical analysis of lattice system. Journal of the Royal Statistical Society, Series B, 36:192–236.

    Google Scholar 

  • Bouman, C. and Liu, B. 1991. Multiple resolution segmentation of textured images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13(2):99–113.

    Google Scholar 

  • Cardy, J. 1996. Scaling and Renormalization in Statistical Physics. Cambridge University Press.

  • Chatterjee, S. 1993. Classification of natural textures using Gaussian Markov random fields models. In Markov Random Fields: Theory and Applications. Academic Press, pp. 159–177.

  • Chen, C.H., Pau, L.F., and Wang, P.S.P. (Eds.). 1993. Handbook of Pattern Recognition and Computer Vision. World Scientific Publishing Company.

  • Cohen, F.S. and Cooper, D.B. 1987. Simple parallel hierarchical and relaxation algorithms for segmenting non causal Markovian random fields. IEEE Trans. on pattern analysis and machine intelligence, 9(2):195–219.

    Google Scholar 

  • Conners, R.W., Trivedi, M.M., and Harlow, C.A. 1984. Segmentation of high-resolution urban scene using texture operators. CVGIP, 25:273–310.

    Google Scholar 

  • Descombes, X. and Prêteux, F. 1993. Topology and parameter estimation in MRF modeling. In SPIE, Neural and Stochastic Methods in Image and Signal Processing II, Vol. 2032, San Diego, pp. 156–166.

    Google Scholar 

  • Descombes, X., Sigelle, M., and Prêteux, F. 1999. Estimating Gaussian Markov random field parameters in a non-stationary framework: Application to remote sensing imaging. IEEE Trans. on Image Processing, 8(4):6–19.

    Google Scholar 

  • Dubes, R.C. and Jain, A.K. 1989. Random fields models in image analysis. Journal of Applied Statistics, 16(2):131–164.

    Google Scholar 

  • Fogel, I. and Sagi, D. 1989. Gabor filters as texture discriminator. Biological Cybernetics, 61:103–113.

    Google Scholar 

  • Forbes, F. and Raftery, A.R. 1999. Bayesian morphology: Fast unsupervised bayesian image analysis. Journal of the American Statistical Association–Theory and Methods, 94(446):555–568.

    Google Scholar 

  • Francos, J.M., Meiri, A.Z., and Porat, B. 1993. A unified texture model based on a 2D Wold-like decomposition. IEEE Trans. on Signal Processing, 41(8):2665–2678.

    Google Scholar 

  • Frigui, H. and Krishnapuram, R. 1996. A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers. In CVPR, San Francisco, pp. 550–555.

  • Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6(6):721–741.

    Google Scholar 

  • Gidas, B. 1989. A renormalization group approach to image processing problems. IEEE Trans. on Pattern Analysis and Machine Intelligence, 11(2):164–180.

    Google Scholar 

  • Gouinaud, C. 1996. Traitement d'images satellitaires pour la détection d'agglomérations. PhD thesis, Ecole Nationale Supérieure des Télécommunications, Telecom Paris 96E035, Paris, FRANCE.

  • Gull, S.F. and Skilling, J. 1984. Maximum entropy method in image processing. Proc. Inst. Elec. Eng. F, 131:646–659.

    Google Scholar 

  • Haralick, R.M. 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786–804.

    Google Scholar 

  • Hoffman, R. and Jain, A.K. 1987. Segmentation and classification of range images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 9(5):608–620.

    Google Scholar 

  • Houzelle, S. and Giraudon, G. 1991. Data fusion using SPOT and SAR images for bridge and urban area extraction. In IGARSS, Vol. 3, Helsinki, pp. 1455–1458.

    Google Scholar 

  • Huang, K. 1998. Quantum Field Theory: From Operators to Path Integrals. John Wiley: New York.

    Google Scholar 

  • Hubel, D.H. 1988. Eye, Brain, and Vision. Freeman.

  • Jain, A.K. and Farrokhnia, F. 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24(12):1167–1186.

    Google Scholar 

  • Julesz, B. and Bergen, J.R. 1987. Textons, the fundamental elements in preattentive vision and perception of textures. In Readings in Computer Vision, Issues, Problems, Principles and Paradigms. Morgan Kaufmann, pp. 243–256.

  • Khinchin, A.I. 1957. Mathematical Foundation of Information Theory. Dover.

  • Krishnapuram, R. and Keller, J.M. 1994. Fuzzy and Possibilistic Clustering Methods for Computer Vision. Vol. 12 of SPIE Institute Series, in Neural and Fuzzy Systems, SPIE-The International Society for Optical Engineering, pp. 133–159.

    Google Scholar 

  • Le Men, H. and Jamet, O. December 94/March 95. Qualité de processus d'interprétation et qualité des résultats: un exemple en cartographie d'occupation du sol. CFC, France, (142/143):182–192.

  • Li, S.Z. 1995. Markov Random Field Modeling in Computer Vision. Springer-Verlag.

  • Lorette, A., Descombes, X., and Zerubia, J. 1998. Urban areas extraction based on texture analysis through a markovian modelling. INRIA Research Report RR-3423 (in French).

  • Lorette, A., Descombes, X., and Zerubia, J. 1999. Texture analysis through markov random fields: Urban areas extraction. In ICIP, Kobe.

  • Malik, J. and Perona, P. 1990. Preattentive texture discrimination with early vision mechanisms. Journal of Optical Society of America, 7(2):923–932.

    Google Scholar 

  • Matsuba, I. 1988. Renormalization group approach to hierarchical image analysis. In ICASSP, Seattle, pp. 1044–1047.

  • Mood, A., Graybill, F., and Boes, D. 1974. Introduction to the Theory of Statistics. McGraw-Hill International Editions (Statistics Series).

  • Nguyen, H.H. and Cohen, P. 1993. Gibbs random fields, fuzzy clustering, and the unsupervised segmentation of textured images, CVGIP, 55(1):1–19.

    Google Scholar 

  • Palubinskas, G., Descombes, X., and Kruggel, F. 1998. An unsupervised clustering method using the entropy minimization. In ICPR, Vol. 2, Australia, pp. 1816–1818.

    Google Scholar 

  • Richard, F., Falzon, F., Zerubia, J., and Giraudon, G. 1998. Segmentation of spot images using markov random fields. In EUSIPCO, Vol. 4, Rhodes, pp. 2493–2496.

    Google Scholar 

  • Ruspini, E.H. 1969. A new approach to clustering. Inform. Control, 15(1):22–32.

    Google Scholar 

  • Schroeder, M. 1990. Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise. W.H. Freeman and Company.

  • Shannon, C.E. 1948. A mathematical theory of communication. Bell Systems Technical Journal, 27(379).

  • Sigelle, M. and Ronfard, R. 1992. Modéles de Potts et relaxation d'images de labels par champs de Markov. Traitement du Signal, 6(9):449–458.

    Google Scholar 

  • Unser, M. 1995. Texture classification and segmentation using wavelet frames. IEEE Trans. on Image Processing, 4(11):1549–1560.

    Google Scholar 

  • Wilson, K. and Kogut, J. 1974. The renormalization group and the ∈ expansion. Physics Report, 12:75–200.

    Google Scholar 

  • Winkler, G. 1995. Image Analysis, Random Fields and Dynamic Monte Carlo Methods: A Mathematical Introduction. Springer-Verlag.

  • Winter, A., Maître, H., Cambou, N., and Legrand, E. 1997. An original multi-sensor approach to scale-based image analysis for aerial and satellite images. In ICIP, volume 2, pages 234–237, Santa-Barbara, 1997.

    Google Scholar 

  • Zadeh, L.A. 1965. Fuzzy sets. Inform. Control, 8:338–353.

    Google Scholar 

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Authors and Affiliations

  1. Ariana, Joint group CNRS/INRIA/UNSA INRIA, 2004 Route Des Luciozes, BP 93, 06902, Sophia Antipolis Codex, France

    A. Lorette, X. Descombes & J. Zerubia

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  1. A. Lorette
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  2. X. Descombes
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  3. J. Zerubia
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Lorette, A., Descombes, X. & Zerubia, J. Texture Analysis through a Markovian Modelling and Fuzzy Classification: Application to Urban Area Extraction from Satellite Images. International Journal of Computer Vision 36, 221–236 (2000). https://doi.org/10.1023/A:1008129103384

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