A two-pass random forests classification of airborne lidar and image data on urban scenes
暂无分享,去创建一个
[1] Li Guo,et al. Contribution of airborne full-waveform lidar and image data for urban scene classification , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[2] Roberto Manduchi,et al. Supervised Parametric Classification of Aerial LiDAR Data , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[3] Avideh Zakhor,et al. Tree Detection in Aerial Lidar and Image Data , 2006, 2006 International Conference on Image Processing.
[4] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[5] Leo Breiman,et al. HALF&HALF BAGGING AND HARD BOUNDARY POINTS , 1998 .
[6] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[7] J. Hyyppä,et al. Automatic detection of buildings from laser scanner data for map updating , 2003 .
[8] Uwe Soergel,et al. ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS , 2008 .
[9] Boris Jutzi,et al. Segmentation of tree regions using data of a full-waveform laser , 2007 .
[10] W. Wagner,et al. 3D vegetation mapping using small‐footprint full‐waveform airborne laser scanners , 2008 .
[11] Christian Früh,et al. Data Processing Algorithms for Generating Textured 3D Building Facade Meshes from Laser Scans and Camera Images , 2005, International Journal of Computer Vision.