期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2002
卷号：XXXIV Part 3 A
页码：35-41
出版社：Copernicus Publications
摘要：Bayesian object recognition is applied to the analysis of complex forest object configurations measured in high-density airborne laser scanning (LIDAR) data. With the emergence of high-resolution active remote sensing technologies, highly detailed, spatially explicit forest measurement information can be extracted through the application of statistical object recognition algorithms. A Bayesian approach to object recognition incorporates a probabilistic model of the active sensing process and places a prior probability model on object configurations. LIDAR sensing geometry is explicitly modelled in the domain of scan space, a three- dimensional analogue to two-dimensional image space. Prior models for object configurations take the form of Markov marked point processes, where pair-wise object interactions depend upon object attributes. Inferences are based upon the posterior distribution of the object configuration given the observed LIDAR. Given the complexity of the posterior distribution, inferences are based upon dependent samples generated via Markov chain Monte Carlo simulation. This algorithm was applied to a 0.21 ha area within Capitol State Forest, WA, USA. Algorithm-based estimates are compared to photogrammetric crown measurements and field inventory data
