A hybrid approach for tree classification in airborne LIDAR data
In this paper we propose a hybrid approach for tree classification in airborne LIDAR (Light Detection and Ranging) data by integrating the point based supervised classification with region-based unsupervised clustering method. Furthermore we propose a novel 3D robust statistics-based shape feature that can overcome the limitations of existing methods in separating building boundary points from tree points. Experimental results show the new algorithm is very effective and can achieve very high accuracy.