Using object-based analysis to derive surface complexity information for improved filtering of airborne laser scanning data
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- 作者：Menglong Yan ; Thomas Blaschke ; Hongzhao Tang ; Chenchao Xiao…
- 关键词：airborne laser scanning ; object ; based analysis ; surface complexity information ; filtering algorithm
- 刊名：Frontiers of Earth Science
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：11
- 期：1
- 页码：11-19
- 全文大小：
- 刊物主题：Earth Sciences, general;
- 出版者：Higher Education Press
- ISSN：2095-0209
- 卷排序：11
文摘
Airborne laser scanning (ALS) is a technique used to obtain Digital Surface Models (DSM) and Digital Terrain Models (DTM) efficiently, and filtering is the key procedure used to derive DTM from point clouds. Generating seed points is an initial step for most filtering algorithms, whereas existing algorithms usually define a regular window size to generate seed points. This may lead to an inadequate density of seed points, and further introduce error type I, especially in steep terrain and forested areas. In this study, we propose the use of objectbased analysis to derive surface complexity information from ALS datasets, which can then be used to improve seed point generation.We assume that an area is complex if it is composed of many small objects, with no buildings within the area. Using these assumptions, we propose and implement a new segmentation algorithm based on a grid index, which we call the Edge and Slope Restricted Region Growing (ESRGG) algorithm. Surface complexity information is obtained by statistical analysis of the number of objects derived by segmentation in each area. Then, for complex areas, a smaller window size is defined to generate seed points. Experimental results show that the proposed algorithm could greatly improve the filtering results in complex areas, especially in steep terrain and forested areas.