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Proceedings Paper

A simple method to improve the SRTM DEM based on Landsat ETM+ Image
Author(s): Xiaobin Cai; Xiaoling Chen; Hui Li; Liqiao Tian; Zhongyi Wu
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Paper Abstract

Shuttle Radar Terrain Mission (SRTM) DEM has become one of digital topographic data sources of the earth because of its high spatial resolution and near-global coverage. However, its widely usage has been limited by some void areas occurred in SRTM DEM, which are mainly related to the water body, spikes or wells. Although they were modified into finished SRTM DEM by using a complicated process by National Geospatial-Intelligence Agency (NGA), in which a lot of void areas could be filled with correct data, some void areas still exited especially in the water area. In addition, the accuracy of the finished SRTM DEM might be hindered because of no global accurate DEM as a reference. And the finished SRTM DEM can't be freely downloaded from Internet also limits its usage in some extent. A simple method to create an edited SRTM DEM based-on Landsat ETM+ image was proposed in this paper. The unfinished SRTM data was firstly re-projected to the UTM projection for matching the Landsat ETM+ image in the same area. Secondly, through analyzing the spectral attributes of water, the water body was accurately extracted from Landsat ETM+ image by using the indices of NDWI and NDVI. Thirdly, the water body in the unfinished SRTM DEM was masked, and the water void areas and non-water void areas were finally separated. The water body void areas were filled with the surrounding minimum elevation data and the non-water void areas were filled by using the method of Kriging interpolation. The results showed that the proposed method could improve the unfinished SRTM DEM, which were proved to be better than CIAT edited SRTM DEM according to the comparison of both visual effect and statistical accuracy.

Paper Details

Date Published: 3 November 2005
PDF: 6 pages
Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60430H (3 November 2005); doi: 10.1117/12.652441
Show Author Affiliations
Xiaobin Cai, Wuhan Univ. (China)
Xiaoling Chen, Wuhan Univ. (China)
Hui Li, Wuhan Univ. (China)
Liqiao Tian, Wuhan Univ. (China)
Zhongyi Wu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6043:
MIPPR 2005: SAR and Multispectral Image Processing
Liangpei Zhang; Jianqing Zhang; Mingsheng Liao, Editor(s)

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