文章基本信息

标题：A Markov Random Field Model for Individual Tree Detection from Airborne Laser Scanning Data
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作者：Junjie Zhang ; Gunho Sohn
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：2010
卷号：XXXVIII - Part 3A
页码：120-125
出版社：Copernicus Publications
摘要：Small-footprint Airborne Laser Scanning (ALS) holds great potential in forest inventory as it surpasses traditional remote sensing techniques in terms of rapid acquisition of 3D information of trees directly. With the increasing availability of high density ALS data, the derivation of more detailed individual tree information, such as tree position, tree height, crown size and tree species, becomes possible from ALS data exclusively. However, single tree detection is a critical procedure for tree-wise analysis in order to retrieve more accurate individual-tree-based parameters. The presented research highlights a novel Markov Random Field to model the configuration of single trees in ALS data in which a global optimum to isolate individual trees can be achieved and addressed difficulties of individual tree detection problem in terms of problem representation and objective function. Firstly, local maxima are overpopulated from the CHM recovered from ALS data using a circular type of window filter with variable size. Then trees are modelled as objects at the centre of the extracted local maxima and attributed with other features retrieved from CHM image. The neighbourhood system is set up by TIN and energy functions are carefully designed to incorporate constraints for penalizing false trees and favour true ones. Finally, the optimal tree models are obtained through an energy minimization process. The method is applied on ALS data acquired from a coniferous forest and experimental results show a good detection rate
关键词：Airborne Laser Scanning; Tree detection; Markov Random Field; Automation; Segmentation