A multi-window texture classification and object-oriented feature extraction method with airborne LiDAR products

High accurate airborne Light Detection and Range (LiDAR) is widely accepted as one kind of survey data sources. However, with the LiDAR products including Digital Elevation Model (DEM), Digital Surface Model (DSM) and intensity image, land use classifications and feature extractions were generally combined with optical images including satellite images or aerial photos using relative segmentations and feature extraction algorithms. In this paper, a multi-window texture classification and object- oriented feature extraction method is proposed using only airborne LiDAR products. Based on the experimental analysis and accuracy statistics, it is an efficient attempt in land cover classification with airborne LiDAR products.