The method of strict slope threshold algorithm is not sufficient to achieve complex object identification or ground features classification from LiDAR data.In this research

artificial intelligence is used to classify the ground features based on the LiDAR height texture.Average elevation image

average intensity image and ground roughness index image are derived from LiDAR points.Then

4 GLCM texture features including entropy

various

second moment and homogeneity texture are measured.Finally

BP-ANNs are used to classify the texture measure into five ground feature types.A coastal area of Zhujiang Delta

South of China

is taken as the study area.The method employed in this research can efficiently work with single LiDAR data source and the accuracy of classification result is > 90%

and the classification accuracy of Maximal Likelihood method（ML） is 86.8% for comparison.When the result of ANNs classification is compared with the result of optical image classification

it can be found that 76.5% sample points are in accord.