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Prof. Mahmoud Salah Mahmoud Goma :: Publications:

Title:
Performance evaluation of classification trees for building detection from aerial images and LiDAR data: a comparison of classification trees models‏
Authors: M Salah, JC Trinder, A Shaker
Year: 2011
Keywords: Not Available
Journal: International Journal of Remote Sensing
Volume: 32
Issue: 20
Pages: 5757-5783
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Not Available
Supplementary materials Not Available
Abstract:

This study assesses the performance of three classification trees (CT) models (Entropy, Gain Ratio and Gini) for building detection by the fusion of airborne laser scanner data and multi-spectral aerial images. Data from four study areas with different sensors and scene characteristics were used to assess the performance of the models. The process of performance evaluation is based on four criteria: model validation and testing; classification accuracies; relative importance of input variables; as well as transferability of classification trees derived from one dataset to another. The lidar point clouds were filtered to generate a Digital Terrain Model (DTM) based on the orthogonal polynomials, and then a Digital Surface Model (DSM) and the Normalized Digital Surface Model (nDSM) were generated. A set of 26 uncorrelated feature attributes were derived from the original aerial images, lidar intensity image, DSM and nDSM. Finally, the three classification trees models were used to classify buildings, trees, roads and ground from aerial images, lidar data and the generated attributes with the most accurate average classifications of 95% being achieved. The Entropy splitting algorithm proved to be a preferable algorithm for building detection from aerial images and lidar data.

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