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Morse description and geometric encoding of DEM data

Publication ,  Journal Article
Solé, A; Caselles, V; Sapiro, G; Arándiga, F
Published in: IEEE International Conference on Image Processing
December 17, 2003

Two complementary geometric structures for the topographic representation of an image are developed in this work. The first one computes a description of the Morse structure of the image, while the second one computes a simplified version of its drainage structure. The topographic significance of the Morse and drainage structures of Digital Elevation Maps (DEM) suggests that they can been used as the basis of an efficient encoding scheme. We combine this geometric representation with an interpolation algorithm and loss-less data compression schemes to develop a compression scheme for DEM. This algorithm permits to obtain compression results while controlling the maximum error in the decoded elevation map, a property that is necessary for the majority of applications dealing with DEM.

Duke Scholars

Published In

IEEE International Conference on Image Processing

Publication Date

December 17, 2003

Volume

2

Start / End Page

235 / 238
 

Citation

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Solé, A., Caselles, V., Sapiro, G., & Arándiga, F. (2003). Morse description and geometric encoding of DEM data. IEEE International Conference on Image Processing, 2, 235–238.
Solé, A., V. Caselles, G. Sapiro, and F. Arándiga. “Morse description and geometric encoding of DEM data.” IEEE International Conference on Image Processing 2 (December 17, 2003): 235–38.
Solé A, Caselles V, Sapiro G, Arándiga F. Morse description and geometric encoding of DEM data. IEEE International Conference on Image Processing. 2003 Dec 17;2:235–8.
Solé, A., et al. “Morse description and geometric encoding of DEM data.” IEEE International Conference on Image Processing, vol. 2, Dec. 2003, pp. 235–38.
Solé A, Caselles V, Sapiro G, Arándiga F. Morse description and geometric encoding of DEM data. IEEE International Conference on Image Processing. 2003 Dec 17;2:235–238.

Published In

IEEE International Conference on Image Processing

Publication Date

December 17, 2003

Volume

2

Start / End Page

235 / 238