ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 3, pp. 65-69

One channel texture based segmentation: application examples

N.V. Rodionova 
Institute of Radioengineering and Electronics of RAS, Fryazino, Vvedensky sq., 1
The paper proposes to use the textural features, obtained from grey level co-occurrence matrix (GLCM), to segment one channel images. Some texture segmentation examples of SAR and optical images are demonstrated.
Keywords: one channel image, segmentation, texture features, second order statistics
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References:

  1. Astafurov V.G., Skorokhodov A.V., Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 65–72.
  2. Kozlov Z.V., Pavlyukov S.S., Sukhanov K.Yu., Gidroakustichnii zhurnal, 2006, No. 3, pp. 25–31.
  3. Mitsel' A.A., Kolodnikova N.V., Protasov K.T., Izv. Tomskogo politekhnich. un-ta, 2005, Vol. 308, No. 1, pp. 65–70.
  4. Caridade C.M.R., Marçal A.R.S., Mendonça T., The use of texture for image classification of black & white air-photographs, (from New Developments and Challenges in Remote Sensing), Rotterdam: Millpress, 2007.
  5. Freeman A., Durden S.L., A three-component scattering model for polarimetric SAR data, IEEE Trans. Geoscience and Remote Sensing, 1998, Vol. 36, No. 3, pp. 963–973.
  6. Haralick R.M., Textural Features for Image Classification, IEEE Trans. Systems, Man, and Cybernetics,1973, Vol. 3, No. 6, pp. 610–621.
  7. Kuplich T., Curran P., Atkinson P., Relating SAR image texture to the biomass of regenerating tropical Forests, International Journal of Remote Sensing, 2005, Vol. 26, No. 21, pp. 4829–4854.
  8. Lee J.-S., Grunes M.R., De Grandi G., Polarimetric SAR speckle filtering and its implication for classification, IEEE Trans. Geoscience and Remote Sensing, 1999, Vol. 37, No. 5, pp. 2363–2373.
  9. De Morais M. C., Junior P.M.P., Paradella W. R., Potential of SAR data (L-hh-hv-vv) to discriminate iron-mineralised laterites in the Amazon Region (Carajás Province) based on textural attributes, Proc. Anais 13 Simpósio Brasileiro de Sensoriamento Remoto, Florianópolis, Brasil. 2007, pp. 2069-2078.
  10. Puissant J., Hirsch J., Weber C., The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery, International Journal of Remote Sensing, 2005, Vol. 26, No. 4, pp. 733–745.
  11. Rodionova N.V., One channel image texture based interpretation, Proc. PolInSAR-2011, Frascati, 2011, CD-ROM.
  12. Soh L.-K., Tsatsoulis C., Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices, IEEE Trans. Geoscience and Remote Sensing, 1999, Vol. 7, No. 2.
  13. Tiwari S., Agarwal S., Trang A., Texture Feature Selection for Buried Mine Detection in Airborne Multispectral Imagery, Proc. IGARSS’2008, Boston, 2008, CD-ROM.
  14. Ulaby F.T., Kouyate F., Brisco B., Williams T.H.L., Textural Information in SAR Images, IEEE Trans. Geoscience and Remote Sensing, 1986, Vol. GE-24 (2), pp. 235–245.