Applying a-priori knowledge for compressing digital elevation models

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Abstract

Up-to-date, some algorithms related to compress digital elevation models (DEMs) or high-resolution DEMs, use wavelet and JPEG-LS encoding approaches to generate compressed DEM files with good compression factor. However, to access the original data (elevation values), it is necessary to decompress whole model. In this paper, we propose an algorithm oriented to compress a digital elevation model, which is based on a sequence of binary images encoded using RLE compression technique, according to a specific height (contour lines). The main goal of our algorithm is to obtain specific parameters of the DEM (altitudes and contours lines) without using a decompression stage, because the information is directly read from the compressed DEM. © Springer-Verlag Berlin Heidelberg 2006.

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Guzmán, G., Quintero, R., Torres, M., & Moreno, M. (2006). Applying a-priori knowledge for compressing digital elevation models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4251 LNAI-I, pp. 614–622). Springer Verlag. https://doi.org/10.1007/11892960_74

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