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

fYear

2012

fDate

22-27 July 2012

Firstpage

5402

Lastpage

5405

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Conference_Location

Munich

ISSN

2153-6996

Print_ISBN

978-1-4673-1160-1

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2012.6352385

Filename

6352385