Advances in the data compression of digital elevation models
The maintenance and dissemination of spatial databases requires efficient strategies for handling the large volumes of data that are now publicly available. In particular, satellite and aerial imagery, radar, LiDAR, and digital elevation models (DEMs) are being utilised by a sizeable user-base, for predominantly environmental applications. The efficient dissemination of such datasets has become a key issue in the development of web-based and distributed computing environments. However, the physical size of these datasets is a major bottleneck in their storage and transmission. The problem is often exaggerated when the data is supplied in less efficient, proprietary or national data formats. This paper presents a methodology for the lossless compression of DEMs, based on the statistical correlation of terrain data in local neighbourhoods. Most data and image compression algorithms fail to capitalise fully on the inherent redundancy in spatial data. At the same time, users often prefer a uniform solution to all their data compression requirements, but these solutions may be far from optimal. The approach presented here can be thought of as a simple pre-processing of the elevation data before the use of traditional data compression software frequently applied to spatial data sets, such as GZIP. Identification and removal of the spatial redundancy in terrain data, with the use of optimal predictors for DEMs and optimal statistical encoders such as Arithmetic Coding, gives even higher compression ratios. Both GZIP and our earlier approach of combining a simple linear prediction algorithm with Huffman Coding are shown to be far from optimal in identifying and removing the spatial redundancy in DEMs. The new approaches presented here typically halve the file sizes of our earlier approach, and give a 4062% improvement on GZIP-compressed DEMs.
dems arithmetic coding linear prediction lagrange multipliers entropy