LiDAR点云支持下地物精细分类的实现方法  

Method of Land Cover Refined Classification Supported by LiDAR Point Clouds

作　　者：董保根[1] 马洪超[2] 车森[3] 解龙根 何乔[1] 

机构地区：[1]93920部队 [2]武汉大学遥感信息工程学院 [3]信息工程大学地理空间信息学院

出　　处：《遥感技术与应用》2016年第1期165-169,共5页Remote Sensing Technology and Application

基　　金：国家自然科学基金项目"属性匹配在多源空间数据融合中的研究"(41201391)资助

摘　　要：在遥感数据分类中,获取精细的地物类别无疑能够传递更加丰富的信息量,进一步加深对遥感数据的理解和解译。在机载LiDAR点云高程数据的支持下,提出并实现了遥感影像上地物精细分类的方法。为保证高精度地同种地物再划分,综合考虑配准、辅助数据源、首次回波、点云密度及影像空间分辨率4种因素,并重点解决了点云密度与影像空间分辨率不匹配的问题,利用决策树显著地提高了影像上建筑物、植被的分类数量,使点云与影像联合分类的优势得到体现,达到了分类精度与地物类别数量相统一的目的。In the process of classification in remote sensing data, acquiring greater refinement of the land cover type can deliver undoubtedly more information and further deepen the comprehension and interpretation for remote sensing data. With the support of point clouds elevation data, the method of refined classification in remote sensing image is proposed and achieved out. In order to gain high accuracy of subdividing the same kind of land cover type,four factors are taken into consideration,which includes registration,supplementary data source,first echo and point clouds density and image spatial resolution, and the focus is placed on dealing with the problems of mismatch between point clouds density and image spatial resolution. Decision tree is developed to improve remarkably the classification quantity of buildings and vegeta- tion in this study,which represents superiority of classification of fusing point clouds and imagery and achieves the desired goal of the unity of classification accuracy and quantity.

关 键 词：机载LIDAR 精细分类 归一化高度 首次回波 决策树 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]