Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data.

Zhishuang Yang,Wanshou Jiang,Bo Tan,Huikun Pei

doi:10.3390/s18103347

Abstract

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. 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. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. 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. 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.

Highlights

In processing digital terrain models (DTM) and 3D city and landscape models, point clouds have become a more and more popular type of data
Common point clouds can be produced by airborne laser scanning (ALS) [1,2] and by dense matching of aerial photographs [3]
Point-based methods use the information of each point with reference to its neighbor, such as eigenvalue-base features, point density values, and the direction of normal vector, or information based on the point itself, such as intensity value and echo-based features, to obtain accurate classification results

Summary

Introduction

In processing digital terrain models (DTM) and 3D city and landscape models, point clouds have become a more and more popular type of data. No matter which method is chosen, the classification of point clouds cannot be ignored. It is the first step in extracting productive geo-information. In some productions, such as DTM generating, points only need to be classified into two classes. In other processes, such as city reconstruction, points require classification into multiple categories. Segment-based methods divide the point cloud into segments first and put the class label into each segment within which all points belong to the same category

Results

Discussion

Conclusion