Combining Features Extracted from Imagery and Lidar Data for Object-oriented Classification of Forest Areas

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, parcelbased 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) 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.