Building extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm

Abstract

In this study, the fusion of Light Detection and Ranging (LiDAR) and hyperspectral data was used to propose a method for building detection. The number of hyperspectral bands was first reduced from 144 to 8 layers using the Linear Discriminant Analysis (LDA) algorithm to remove highly redundant bands and reduce computational costs. Then, these layers were integrated with 4 layers of heights and intensities obtained from the LiDAR data. The fused layers (12 layers) were applied to a Random Forest (RF) algorithm to extract the boundaries of buildings. Finally, two morphological operators were applied to remove the holes on the buildings’ roofs and repair their boundaries. A comparison was also performed between the results obtained by the proposed method and the reference study in this field [Debes et al. 2014]. The proposed method demonstrated a better accuracy for building detection in a much shorter time compared to the refer ence method. The values of 97% and 96% were obtained for producer and user accuracies, respectively. Overall, the method presented in this study proved to have a high potential for building extraction.