A New Wavelet-Domain HMTseg Algorithm for Remotely Sensed Image Segmentation

  • Qiang Sun
  • Biao Hou
  • Li-cheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


A new wavelet-domain HMTseg method is proposed, which fuses the segmentation results at coarse and fine scales with a new and feasible context model together with one preprocessing of raw segmentations at different scales. Compared to the original HMTseg method, the new method not only lays emphasis on the performance from coarse-scale segmentation, preserves the main outlines of the homogeneous regions in an image, and thus achieves good region consistency of segmentation, but also take into account the information from fine-scale segmentation, thus improves the accuracy of boundary localization of segmentation and enables the discrimination of small targets in an image, which is desirable for interpretation of remotely sensed images. Experiments on remotely sensed images, including aerial photos and SAR images, demonstrate that the proposed method can effectively take into consideration both the region consistency and the accuracy of boundary localization of segmentation performance, and give better segmentation results.


Class Label Gaussian Mixture Model Wavelet Coefficient Synthetic Aperture Radar Synthetic Aperture Radar Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-Based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans. on Signal Processing 46, 886–902 (1998)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Choi, H., Baraniuk, R.G.: Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models. IEEE Trans. on Image Processing 10, 1309–1321 (2001)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Venkatachalam, V., Choi, H., Baraniuk, R.G.: Multiscale SAR Image Segmentation Using Wavelet-Domain Hidden Markov Tree Models. In: Proc. of SPIE, vol. 4053, pp. 1605–1611 (2000)Google Scholar
  4. 4.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  5. 5.
    The USC-SIPC Image Database, Available
  6. 6.
    Sandia Synthetic Aperture Radar Imagery Repository, Available

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qiang Sun
    • 1
  • Biao Hou
    • 1
  • Li-cheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

Personalised recommendations