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.
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