Title :
Advanced methods for automated object extraction from LiDAR in urban areas
Author :
Rottensteiner, Franz
Author_Institution :
Inst. of Photogrammetry & Geoinf., Leibniz Univ. Hannover, Hannover, Germany
Abstract :
This paper gives an overview about advanced techniques for classification and object detection that are being adopted for urban object detection from LiDAR data. The paper covers local supervised classifiers such as AdaBoost, SVM and Random Forests, statistical models of context such as Markov Random Fields and Conditional Random Fields, and sampling techniques. The relevance of features is also discussed. Applications include DTM generation and the extraction of buildings, trees, and low vegetation.
Keywords :
Markov processes; geophysical image processing; image classification; learning (artificial intelligence); object detection; optical radar; sampling methods; statistical analysis; support vector machines; AdaBoost; DTM generation; LIDAR data; Markov random fields; SVM; advanced automated object extraction methods; conditional random fields; local supervised classifiers; object classification; object detection; random forests; sampling techniques; statistical models; urban areas; Buildings; Data models; Laser radar; Remote sensing; Support vector machines; Urban areas; Vegetation; LiDAR; Object detection; Urban areas;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6352385
