AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS - Archive ouverte HAL Access content directly
Conference Papers Year : 2009

AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS

Abstract

Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they return measures of variable importance for each class. The feature selection is obtained by backward elimination of features depending on their importance. This is crucial to analyze the relevance of each lidar feature for the classification of urban scenes. The Random Forests classification using selected variables provide an overall accuracy of 94.35%.
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Dates and versions

hal-02384719 , version 1 (28-11-2019)

Identifiers

  • HAL Id : hal-02384719 , version 1

Cite

Nesrine Chehata, Li Guo, Clément Mallet. AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS. Laserscanning, Sep 2009, Paris, France. ⟨hal-02384719⟩

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