Coupled CRFs for estimating the underlying ground surface from airborne LiDAR data

Airborne laser scanners (LiDAR) return point clouds of millions of points imaging large regions. It is very challenging to recover the bare earth, i.e., the surface remaining after the buildings and vegetative cover have been identified and removed; manual correction of the recovered surface is very costly. Our solution combines classification into ground and non-ground with reconstruction of the continuous underlying surface. We define a joint model on the class labels and estimated surface, $p(\vc,\vz|\vx)$, where $c_i \in \{0,1\}$ is the label of point $i$ (ground or non-ground), $z_i$ is the estimated bare-earth surface at point $i$, and $x_i$ is the observed height of point $i$. We learn the parameters of this CRF using supervised learning. The graph structure is obtained by triangulating the point clouds. Given the model, we compute a MAP estimate of the surface, $\arg \max p(\vz|\vx)$, using the EM algorithm, treating the labels $\vc$ as missing data. Extensive testing shows that the recovered surfaces agree very well with those reconstructed from manually corrected data. Moreover, the resulting classification of points is competitive with the best in the literature.