Land cover classifi cation using LiDAR height texture and ANNs
基于LiDAR高度纹理和神经网络的地物分类

The method of strict slope threshold algorithm is not suffi cient to achieve complex object identifi cation or ground features classifi cation from LiDAR data. In this research, artifi cial 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 fi ve 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 effi ciently work with single LiDAR data source and the accuracy of classifi cation result is > 90%, and the classifi cation accuracy of Maximal Likelihood method (ML) is 86.8% for comparison. When the result of ANNs classifi cation is compared with the result of optical image classifi cation, it can be found that 76.5% sample points are in accord.