Integration of Near Infrared Image and Probabilistic Classifier to Increase the Classification Accuracy of Point Clouds

Essential to the establishment of such 3D spatial information are the laser scanning technology to obtain high-precision 3D point clouds and the photography-metric camera to obtain high-resolution multispectral image information. TLS (Terrestrial laser scanner) is a high precision positioning technique to monitor the behavior and change of structures and natural topography. 3D point clouds of natural environments relevant to problems in geomorphology (rivers, coastal environments, cliffs, …) often require classification of the data into elementary relevant classes12. There are such old techniques to classify massive point clouds as NDVI and PCA (Principal Component Analysis). Although they have many different advantages, it is extremely difficult for them to maximize their advantages to function as stand-alone techniques and overcome their disadvantages. This study thus set out to investigate the integration of NDVI and a probabilistic classifier PCA, SVM (Support Vector Machine), LDA (Linear Discriminant Analysis))1 to obtain land cover information in a natural state through TLS and improve the classification accuracy of resulting massive point clouds.