![]() In processing digital terrain models (DTM) and 3D city and landscape models, point clouds have become a more and more popular type of data. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. ![]() Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. ![]() Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. It is quite a challenge when facing complex observed scenes and irregular point distributions. The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing.
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