The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 97–104, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-97-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 97–104, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-97-2015

  10 Dec 2015

10 Dec 2015

COMPARISON OF DIFFERENT VEGETATION INDICES FOR VERY HIGH-RESOLUTION IMAGES, SPECIFIC CASE ULTRACAM-D IMAGERY

M. Barzegar1, H. Ebadi2, and A. Kiani2 M. Barzegar et al.
  • 1K. N. Toosi University of Technology, Tehran, Iran
  • 2Geodesy and Geomatics Eng. faculty, K. N. Toosi University of Technology, Tehran, Iran

Keywords: Vegetation indices, High-resolution images, UltraCam, DVI

Abstract. Today digital aerial images acquired with UltraCam sensor are known to be a valuable resource for producing high resolution information of land covers. In this research, different methods for extracting vegetation from semi-urban and agricultural regions were studied and their results were compared in terms of overall accuracy and Kappa statistic. To do this, several vegetation indices were first tested on three image datasets with different object-based classifications in terms of presence or absence of sample data, defining other features and also more classes. The effects of all these cases were evaluated on final results. After it, pixel-based classification was performed on each dataset and their accuracies were compared to optimum object-based classification. The importance of this research is to test different indices in several cases (about 75 cases) and to find the quantitative and qualitative effects of increasing or decreasing auxiliary data. This way, researchers who intent to work with such high resolution data are given an insight on the whole procedure of detecting vegetation species as one of the outstanding and common features from such images. Results showed that DVI index can better detect vegetation regions in test images. Also, the object-based classification with average 93.6% overall accuracy and 86.5% Kappa was more suitable for extracting vegetation rather than the pixel-based classification with average 81.2% overall accuracy and 59.7% Kappa.