A Watershed-Based Segmentation Technique for Multiresolution Data

  • Giuseppe Masi
  • Giuseppe Scarpa
  • Raffaele Gaetano
  • Giovanni Poggi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

A new watershed-based technique is proposed for the segmentation of multiresolution remote-sensing images. These images are composed by a high-resolution panchromatic band and a low-resolution multispectral set. To achieve a segmentation with the high resolution of the panchromatic image and the high accuracy granted by the spectral information, the two components are processed jointly, using both spectral and morphological properties. In addition, a fully automatic marker generation procedure is introduced to reduce the oversegmentation typical of watershed methods. Experiments on WorldView-2 multiresolution images demonstrate the potential of the technique.

Keywords

Panchromatic Image Crest Line Remote Sensing Symposium Urban Impervious Surface High Local Accuracy 
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.

References

  1. 1.
    Xiao, P., Feng, X., Zhao, S., She, J.: Multispectral ikonos image segmentation based on texture marker-controlled watershed algorithm. In: SPIE 6790, MIPPR (2007)Google Scholar
  2. 2.
    Cagnazzo, M., Poggi, G., Verdoliva, L.: Region-based transform coding of multispectral images. IEEE Transactions on Image Processing 16, 2916–2926 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Parrilli, S., Poderico, M., Angelino, C.V., Scarpa, G., Verdoliva, L.: A nonlocal approach for SAR image denoising. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010, pp. 726–729 (2010)Google Scholar
  4. 4.
    Cagnazzo, M., Parrilli, S., Poggi, G., Verdoliva, L.: Improved Class-Based Coding of Multispectral Images With Shape-Adaptive Wavelet Transform. IEEE Geoscience and Remote Sensing Letters 4(4), 566–570 (2007)CrossRefGoogle Scholar
  5. 5.
    Li, P., Guo, J., Song, B., Xiao, X.: A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(1), 103–116 (2011)CrossRefGoogle Scholar
  6. 6.
    Bova, N., Ibanez, O., Cordon, O.: Image Segmentation Using Extended Topological Active Nets Optimized by Scatter Search. IEEE Computational Intelligence Magazine 8(1), 16–32 (2013)CrossRefGoogle Scholar
  7. 7.
    D’Elia, C., Marrocco, C., Molinara, M., Poggi, G., Scarpa, G., Tortorella, F.: Detection of microcalcifications clusters in mammograms through TS-MRF segmentation and SVM-based classification. In: 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 742–745 (2004)Google Scholar
  8. 8.
    Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., Bruce, L.M.: Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest. IEEE Transactions on Geoscience and Remote Sensing 45(10), 3012–3021 (2007)CrossRefGoogle Scholar
  9. 9.
    Beucher, S., Lantuejoul, C.: Use of Watersheds in Contour Detection. In: International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation, Rennes, France (September 1979)Google Scholar
  10. 10.
    Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5), 898–916 (2011)CrossRefGoogle Scholar
  11. 11.
    Gaetano, R., Masi, G., Scarpa, G., Poggi, G.: A marker-controlled watershed segmentation: Edge, mark and fill. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, pp. 4315–4318 (2012)Google Scholar
  12. 12.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  13. 13.
    Comaniciu, D., Meer, P.: Mean Shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  14. 14.
    Mikes, S., Haindl, M., Scarpa, G.: Remote sensing segmentation benchmark. In: 7th IAPR International Workshop on Pattern Recognition in Remote Sensing, PRRS 2012, Tsukuba Science City, Japan (November 2012)Google Scholar
  15. 15.
    Scarpa, G., Haindl, M.: Unsupervised texture segmentation by spectral-spatial-independent clustering. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 151–154 (August 2006)Google Scholar
  16. 16.
    Gaetano, R., Scarpa, G., Poggi, G.: Hierarchical texture-based segmentation of multiresolution remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing 47(7), 2129–2141 (2009)CrossRefGoogle Scholar
  17. 17.
    Yuan, J., Wang, D.L., Li, R.: Remote Sensing Image Segmentation by Combining Spectral and Texture Features. IEEE Transactions on Geoscience and Remote Sensing (to appear) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Giuseppe Masi
    • 1
  • Giuseppe Scarpa
    • 1
  • Raffaele Gaetano
    • 2
  • Giovanni Poggi
    • 1
  1. 1.DIETIUniversity Federico II of NaplesItaly
  2. 2.TSI DepartmentTELECOM-ParisTechFrance

Personalised recommendations