ADABOOST-BASED FEATURE RELEVANCE ASSESSMENT IN FUSING LIDAR AND IMAGE DATA FOR CLASSIFICATION OF TREES AND VEHICLES IN URBAN SCENES

• Main Authors: Y. Wei, W. Yao, J. Wu, M. Schmitt, U. Stilla
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-07-01
• Series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-7/323/2012/isprsannals-I-7-323-2012.pdf
• ISSN: 2194-9042; 2194-9050

In this paper, we present an integrated strategy to comprehensively evaluate the feature relevance of point cloud and image data for classification of trees and vehicles in urban scenes. First of all, point cloud and image data are co-registered by backprojection with available orientation parameters if necessary. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per pixel/point. Then, various spatial-statistical and radiometric features can be extracted using a cylindrical volume neighborhood. Classification results as labeled pixels can be acquired from the classifier, and after appropriate refinements we obtain the objects of trees and vehicles. Compared to other methods which have assessed the classification and relevance simultaneously using a single classifier, we first introduce AdaBoost classifier combined with contribution ratio to provide both classification results and measures of feature relevance, and then utilize Random Forest classifier to evaluate and compare the feature relevance from a more independent viewpoint. In order to confirm the accuracy and reliability of classification and feature relevance results, we consider not only characteristics of the classifiers itself, but also errors of data co-registration and alterable parameters. We apply the procedure to two different datasets. In the dataset requiring co-registration a-priori, the AdaBoost classifier even achieves a great accuracy of 96.99% for trees and 83.45% for vehicles. The quantitative results of feature relevance assessment highlight the most important features for classification of tree covers and vehicles, such as NDVI, LiDAR intensity, planarity and entropy. By comparative analysis of the two independent approaches, the reliable and consistent feature selection for classification of trees and vehicles from LiDAR and image data could be validated and achieved, being unrelated to the classifiers.