Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Improving Urban Vegetation Classification by Including Height Information Derived from High-Spatial Resolution Stereo Imagery

  • Myeong, Soo-Jeong (Program in Environmental and Resource Engineering SUNY College of Environmental Science and Forestry)
  • Published : 2005.10.01
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Abstract

Vegetation classes, especially grass and tree classes, are often confused in classification when conventional spectral pattern recognition techniques are used to classify urban areas. This paper reports on a study to improve the classification results by using an automated process of considering height information in separating urban vegetation classes, specifically tree and grass, using three-band, high-spatial resolution, digital aerial imagery. Height information was derived photogrammetrically from stereo pair imagery using cross correlation image matching to estimate differential parallax for vegetation pixels. A threshold value of differential parallax was used to assess whether the original class was correct. The average increase in overall accuracy for three test stereo pairs was $7.8\%$, and detailed examination showed that pixels reclassified as grass improved the overall accuracy more than pixels reclassified as tree. Visual examination and statistical accuracy assessment of four test areas showed improvement in vegetation classification with the increase in accuracy ranging from $3.7\%\;to\;18.1\%$. Vegetation classification can, in fact, be improved by adding height information to the classification procedure.

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References

  1. Avery, T. E, 1977. Interpretation of Aerial Photographs. 3rd Ed. Burgess publishing company, Minneapolis, Minnesota. 52 p
  2. Berberoglu, S., Llyoid, C. D., Atkinson, P. M., and P. J. Curran, 2000. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean, Computers and Geoscience, 26: 385-396 https://doi.org/10.1016/S0098-3004(99)00119-3
  3. Emerge, 2003. www.emergeweb.com
  4. Gruen, A. W., 1985. Adaptive least squares correlation: a powerful image matching technique, J. of Photogrammetry, Remote Sensing and Cartography, 14(3): 175-187
  5. Gruen, A. W. and E. P. Baltsavias, 1988. Geometrically Constrained Multiphoto Matching, Photogrammeteric Engineering and Remote Sensing, 54(5): 633-641
  6. Heipke, C, 1992. A Global Approach for least-squares image matching and surface reconstruction in object space, Photogrammetric Engineering and Remote Sensing, 58(3): 317-323
  7. Kreyszig, E., 1979. Introductory Mathematical Statistics: Principles and Methods, John Wiley, New York, 470 p
  8. Lillesand, T. M. and R. W. Kiefer, 2000. Remote Sensing and Image Interpretation, John Wiley & Sons, Inc. New York
  9. Myeong, S., D. J. Nowak, P. F. Hopkins, and R. J. Brock, 2003. Urban cover classification using digital, high-spatial resolution aerial imagery, Urban Ecosystems, 5: 243-256 https://doi.org/10.1023/A:1025687711588
  10. Rosenholm, D., 1987. Multi-Point Matching using the Least-Squares technique for evaluation of three-dimensional models, Photogrammetric Engineering and Remote Sensing, 53(6): 621626
  11. Ryherd, S. and C. Woodcock, 1996. Combining spectral and texture data in the segmentation of remotely sensed images, Photogrammetric Engineering and Remote Sensing, 62(2): 181-194
  12. Saleh, R. and F. L. Scarpace, 1995. Multidimensional matching techniques for stereophotogrammetric mapping, Proc. of the ASPRS/ACSM Annual Conference, Charlotte, NC. Feb 27 - Mar 2,1995. Vol 2, 450-452
  13. Schenk, T., 1999. Digital Photogrammetery, TerraScience. Laurelville, OH
  14. Stehman, S., 1995. Thematic map accuracy assessment from the perspective of finite population sampling, International Journal of Remote Sensing, 16(3): 589-593 https://doi.org/10.1080/01431169508954425
  15. Stehman, S., 1999. Basic probability sampling designs for thematic map accuracy assessment, International Journal of Remote Sensing, 20(12): 2423-2441 https://doi.org/10.1080/014311699212100
  16. Stefanov, W. L., M. S. Ramsey, and P. R. Christensen, 2001. Monitoring urban land cover change an expert system approach to land cover classification of semiarid to arid urban centers, Remote Sensing of Environment, 77: 173-185 https://doi.org/10.1016/S0034-4257(01)00204-8
  17. Toth, C. and A. Krupnik, 1994. Experiences with automatic image patch matching. Proc. of the ASPRS/ACSM Annual Conference, Reno, NY, April 25-28, 1: 644-651
  18. Wolf, P. R., 1983. Elements of Photogrammetry. McGraw-Hill, Inc., New York
  19. Wolf, P. R. and B. A., DeWitt, 2000. Elements of Photogrammetry with Applications in GIS. 3rd Ed. McGraw-Hill, Inc. New York
  20. Zhang, Y., 2001. Texture-integrated classification of urban treed areas in high-resolution colorinfrared imagery, Photogrammetric Engineering and Remote Sensing, 67(12): 1359-1365