Lidar information extraction by attribute filters with partial reconstruction

Recent advances in airborne light detection and ranging (LiDAR) technology allow us to rapid measure the topographical information over large areas. LiDAR remote sensed data has been widely used in many applications, e.g. forest management, urban planning, disaster predictions, etc. However, extracting useful information from LiDAR data remains challenging, especially in the urban remote sensing, where many objects have the same elevation and are connected, such as road and parking lots, trees and buildings. In this work, we present a new method to extract geometric and textural information from LiDAR data by using attribute filters with partial reconstruction. The proposed method can separate the connected objects and better model the geometric and textural information than traditional connected filters (e.g. attribute filters). Experimental results on LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using original LiDAR data or attribute profiles computed by traditional attribute filters, with the proposed method, overall classification accuracies were improved by 35% and 12%, respectively.