数字高程模型压缩研究进展浅析

Research progress review on DEM data compression

数字高程模型(DEM)是遥感技术、地理信息系统等领域开展研究的重要数据源,近年来成为地理信息技术的一个研究热点,基于DEM数据压缩算法得到广泛的研究,本文结合大量国内外文献,就基于树结构编码、人工神经网络、小波变换、基于判别规则(指标)的DEM压缩算法进行论述;并指出DEM模型构建以及数据压缩必须更加注重地形因素,注重基于组合压缩算法以及基于数据压缩、存储、传输、优化显示于一体的广义DEM数据压缩方法的研究。

Digital Elevation Model （DEM） is the most important data sources of remote sensing （RS） and geographic information system （GIS）. In recent years, it is becoming the hot research topic of geographic information technology, and the algorithm of DEM data compression are widely studied by scholars both at home and abroad. Various kinds of effective compression algorithms are pro- posed, such as tree structure coding, artificial neural network, wavelet transform, combination compression algorithm and the compres- sion algorithm based on the discriminate rule （index）. These algorithms of DEM data compression were analyzed and discussed in de- tail in the paper. The compression algorithms with combining related compression algorithms could make full use of their respective ad- vantages. It is necessary to do some further research on combined compression method and generalized DEM compression method based on data compression, storage, transmission, and optimal demonstration. It is very important to pay more attention to the terrain factors in establishment and data compression of DEM.