[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features

  • Conference paper
Advances in Visual Computing (ISVC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5359))

Included in the following conference series:

Abstract

Image segmentation is a fundamental process in remote sensing applications, whose main purpose is to allow a meaningful discrimination among constituent regions of interest. This work presents a novel image segmentation method based on wavelet transforms for extracting a number of color and texture features from the images. Traditional feature extraction techniques based on individual pixels usually demand high computational cost. To reduce such computational cost, while achieving high-quality results, our approach is composed of two main stages. Initially, the image is decomposed into blocks of pixels and a wavelet transform is applied to each block to identify homogeneous regions of the image, assigning the entire block to a class. A refinement stage is applied to the remaining pixels which belong to blocks marked as heterogenous in the first stage. The developed method, tested on several remote sensing images and compared to a well known image segmentation method, presents high adaptability to image regions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2000)

    MATH  Google Scholar 

  2. Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 800–810 (2001)

    Article  Google Scholar 

  3. Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision 70, 109–131 (2006)

    Article  Google Scholar 

  4. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: Interactive Foreground Extraction using Iterated Graph Cuts. ACM Transactions on Graphics 23, 309–314 (2004)

    Article  Google Scholar 

  5. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000)

    Google Scholar 

  6. Schwartz, W.R., Pedrini, H.: Color Textured Image Segmentation Based on Spatial Dependence Using 3D Co-occurrence Matrices and Markov Random Fields. In: 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic, pp. 81–87 (2007)

    Google Scholar 

  7. Cheng, Y.C., Chen, S.Y.: Image Classification Using Color, Texture and Regions. Image and Vision Computing 21, 759–776 (2003)

    Article  Google Scholar 

  8. Liapis, S., Sifakis, E., Tziritas, G.: Colour and Texture Segmentation Using Wavelet Frame Analysis, Deterministic Relaxation, and Fast Marching Algorithms. Journal of Visual Communication and Image Representation 15, 1–26 (2004)

    Article  Google Scholar 

  9. Russ, J.C.: The Image Processing Handbook. CRC Press and IEEE Press, Boca Raton (1998)

    MATH  Google Scholar 

  10. Arivazhagan, S., Ganesan, L.: Texture Classification Using Wavelet Transform. Pattern Recognition Letters 24, 1513–1521 (2003)

    Article  MATH  Google Scholar 

  11. Idrissa, M., Acheroy, M.: Texture Classification Using Gabor Filters. Pattern Recognition Letters 23, 1095–1102 (2002)

    Article  MATH  Google Scholar 

  12. Singh, M., Singh, S.: Spatial Texture Analysis: A Comparative Study. In: International Conference on Pattern Recognition, vol. 1, pp. 676–679 (2002)

    Google Scholar 

  13. Sun, J., Gu, D., Zhang, S., Chen, Y.: Hidden Markov Bayesian Texture Segmentation Using Complex Wavelet Transform. In: IEE Proceedings on Vision, Image and Signal Processing, vol. 151, pp. 215–223 (2004)

    Google Scholar 

  14. Palm, C.: Color Texture Classification by Integrative Co-occurrence Matrices. Pattern Recognition 37, 965–976 (2004)

    Article  Google Scholar 

  15. Daubechies, I.: Ten Lectures on Wavelets. CBMS-NSF Reg. Conf. Series in Applied Math. SIAM, Philadelphia (1992)

    Book  MATH  Google Scholar 

  16. Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  17. Pun, C.M.: Rotation-invariant texture feature for image retrieval. Computer Vision and Image Understanding 89, 24–43 (2003)

    Article  MATH  Google Scholar 

  18. Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Transactions on Image Processing 4, 1459–1560 (1995)

    Google Scholar 

  19. Gose, E., Johnsonbaugh, R., Jost, S.: Pattern Recognition and Image Analysis. Prentice-Hall, Inc., Upper Saddle River (1996)

    Google Scholar 

  20. VisTex: Vision Texture Database (2008), http://vismod.media.mit.edu/vismod/imagery/VisionTexture/distribution.html

  21. Liu, C., Frazier, P., Kumar, L.: Comparative Assessment of the Measures of Thematic Classification Accuracy. Remote Sensing of Environment 107, 606–616 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Federal University of Paraná, Curitiba, PR, Brazil, 81531-990

    Ricardo Dutra da Silva

  2. Institute of Computing, University of Campinas, Campinas, SP, Brazil, 13084-971

    Rodrigo Minetto & Helio Pedrini

  3. Department of Computer Science, University of Maryland, College Park, MD, USA, 20742-327

    William Robson Schwartz

Authors
  1. Ricardo Dutra da Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo Minetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Robson Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Mitsubishi Electric Research Laboratories, P.O. Box 02139, Cambridge, MA, USA

    Fatih Porikli

  7. Computer and Information Science and Engineering, University of Florida, P.O. Box, FL 32611-6120, Gainsville, USA

    Jörg Peters

  8. IBM T.J. Watson Research Center, 19 Skyline Drive, NY 10532, Hawthorne, USA

    James Klosowski

  9. 128 Memorial Mall, Stewart B001, IN 47907, West Lafayette, USA

    Laura Arns

  10. Denver Museum of Nature and Space, 2001 Colorade Blvd. Denver,, CO 80205, USA

    Yu Ka Chun

  11. Departmen of Computer Science,, NC State University, Campus Box 8206, NC 27695-8206, Raleigh, USA

    Theresa-Marie Rhyne

  12. Los Alamos National Labs, P.O. Box 1663, NM 87545, Los Alamos, USA

    Laura Monroe

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

da Silva, R.D., Minetto, R., Schwartz, W.R., Pedrini, H. (2008). Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89646-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89645-6

  • Online ISBN: 978-3-540-89646-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation