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 - 4/C7
出版社：Copernicus Publications
摘要：During the last several years, lidar has become a widely used technique for data collection from the earth surface and vegetation canopy being the large volume of high density lidar data the main drawback for its interpretation and analysis. In addition, parcel- based segmentation of high-resolution remotely sensed data can provide convenient and useful spatial and structural information. In this paper, a methodology for semi-automatic updating of forest land use/land cover geo-spatial databases, using high spatial resolution imagery and lidar data, is presented. High spatial resolution multispectral imagery and low density lidar data (0.5 points/m 2 ) has been employed. Cartographic limits from a cadastre geospatial database have been used in order to segment the territory and create analysis objects. The objects are characterized using a set of descriptive features: spectral, structural, shape and texture features computed from the multispectral image. These features are combined with 3D features derived from lidar data: density profiles based indices and statistics from point cloud, intensity values and normalized digital surface models. The lidar descriptive features proposed provide a more intuitive interpretation of the vegetation canopy structure than the raw data. The classification is performed using the decision trees technique combined with the boosting multi-classifier. Classification assessment is done by using ground truth data
关键词：Object-oriented classification; feature extraction; high-resolution imagery; lidar; land cover mapping
