利用航空影像生成DEM的多层次整体优化方法  

Multi-level Global Optimization Approach for DEM Generation from Aerial Imagery

作　　者：柴登峰[1,2] 彭群生[1] 

机构地区：[1]浙江大学CAD&CG国家重点实验室,杭州310027 [2]浙江大学空间信息技术研究所,杭州310027

出　　处：《中国图象图形学报》2009年第7期1458-1462,共5页Journal of Image and Graphics

基　　金：国家自然科学基金项目(60673032);国家高技术研究发展计划(863)项目(2007AA01Z316);浙江省教育厅科研基金项目(Y200804874)

摘　　要：利用航空影像生成数字高程模型(DEM)通常包括两个步骤,首先采用特征匹配方法建立稀疏对应并确定其高程,然后采用内插方法生成密集高程数据。匹配过程没有进行整体优化,内插过程往往引入误差,它们都将降低DEM生成质量。将DEM生成表述为像素标号问题,匹配影像直接生成DEM,不但进行整体优化而且去除内插对DEM质量的影响。在此基础上,提出了层次标号策略,首先构造多分辨率DEM,然后确定最低分辨率高度场,并自顶向下逐步确定剩余分辨率高度场。多分辨率表达形式和层次标号策略提高了求解效率,能处理数据量非常大的航空影像。利用较高层标号结果限定较低层节点的状态空间,不但提高搜索效率而且提高求解可靠性。实验结果表明上述方法能够生成高质量DEM。Traditional approaches to generate digital elevation model（DEM） from aerial imagery consist of two steps. The first step establishes feature eorrespondences and determines their height, and the second interpolates height to generate dense DEM. Because the first step does not apply global optimization and the second step usually introduces interpolation error, they impair the quality of DEM. This paper describes DEM as Markov random fields, formulates DEM generation as pixel labeling. It generates DEM in a global optimization framework and does not need interpolation. Then, this paper constructs multi-resolution Height Fields and proposes a multi-level pixel labeling strategy. It determines the Height Fields on the highest level at first, and then determines the Height Fields on the rest levels step by step. It improves efficiency greatly. At last, this paper modified Belief Propagation algorithm to determine Height Fields on a specific level. It passes Height Fields on the higher level to the lower level, restricts the possible height and reduces the search space greatly. As a result, it improves both efficiency and quality. Experimental results have shown that high quality DEM have been generated by the proposed approach.

关 键 词：DEM生成 立体匹配 马尔可夫随机场 整体优化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]