A classifying method analysis on the number of returns for given pulse of post-earthquake airborne LiDAR data

Abstract

Compared to remote sensing image, post-earthquake airborne Light Detection And Ranging (LiDAR) point cloud data contains a high-precision three-dimensional information on earthquake disaster which can improve the accuracy of the identification of destroy buildings. However after the earthquake, the damaged buildings showed so many different characteristics that we can't distinguish currently between trees and damaged buildings points by the most commonly used method of pre-processing. In this study, we analyse the number of returns for given pulse of trees and damaged buildings point cloud and explore methods to distinguish currently between trees and damaged buildings points. We propose a new method by searching for a certain number of neighbourhood space and calculate the ratio(R) of points whose number of returns for given pulse greater than 1 of the neighbourhood points to separate trees from buildings. In this study, we select some point clouds of typical undamaged building, collapsed building and tree as samples from airborne LiDAR point cloud data which got after 2010 earthquake in Haiti MW7.0 by the way of human-computer interaction. Testing to get the Rvalue to distinguish between trees and buildings and apply the R-value to test testing areas. The experiment results show that the proposed method in this study can distinguish between building (undamaged and damaged building) points and tree points effectively but be limited in area where buildings various, damaged complex and trees dense, so this method will be improved necessarily.