Deep learning classification of airborne multispectral LiDAR data using sample generation method

doi:10.13474/j.cnki.11-2246.2021.365

Abstract

Abstract: An airborne multispectral LiDAR system, which can quickly and accurately obtain the spatial geometry and spectral information of ground objects, provides a new data source for ground coverage classification and target recognition. In recent years, a series of breakthroughs have been achieved in deep learning algorithms based on 3D point cloud. However, it is difficult to directly input irregular original point cloud data into deep learning models for point-based classification. In this paper, a sample generation method based on PFS-KNN is proposed for deep learning based classification models using airborne multispectral LiDAR data. The method first normalizes the input data, and then farthest point sampling method and k-nearest neighbor method are used to generate a series of training sample data sets with regular size from the input data. Experiments with the airborne multi-spectral LiDAR data show that the samples generated by the proposed method not only meet the input data format required by the convolutional neural network, but also ensure the complete coverage of the input scene.

Key words: multispectral LiDAR, point cloud samples, deep learning, object classification, sample size

CLC Number:

P237

ZHAO Peiran, GUAN Haiyan, LI Dilong, JING Zhuangwei, YU Yongtao. Deep learning classification of airborne multispectral LiDAR data using sample generation method[J]. Bulletin of Surveying and Mapping, 2021, 0(12): 16-21.

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