Volume XXXIX-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 203-208, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-203-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 203-208, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-203-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

RANDOM FORESTS-BASED FEATURE SELECTION FOR LAND-USE CLASSIFICATION USING LIDAR DATA AND ORTHOIMAGERY

H. Guan1, J. Yu2, J. Li2,1, and L. Luo3 H. Guan et al.
  • 1GeoSTARS Lab, Department of Geography and Environmental Management, University of Waterloo, 200 University Ave. West, Waterloo, ON, Canada N2L 3G1
  • 2GeoSTARS Group, School of Information Science and Engineering, Xiamen University, 422 Siming Road South, Xiamen, Fujian, China 361005
  • 3China Transport Telecommunication & information Center, Beijing, China

Keywords: Lidar, imagery, Random Forests, Classification, Feature selection

Abstract. The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best features for land-use classification is challenging. Random Forests, a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Especially, it can provide the measure of variable importance. Thus, in this study the performance of the Random Forests-based feature selection for urban areas was explored. First, we extract features from lidar data, including height-based, intensity-based GLCM measures; other spectral features can be obtained from imagery, such as Red, Blue and Green three bands, and GLCM-based measures. Finally, Random Forests is used to automatically select the optimal and uncorrelated features for landuse classification. 0.5-meter resolution lidar data and aerial imagery are used to assess the feature selection performance of Random Forests in the study area located in Mannheim, Germany. The results clearly demonstrate that the use of Random Forests-based feature selection can improve the classification performance by the selected features.