LiDAR Data Classification Based on Automatic Designed CNN

Abstract

Recently, convolutional neural networks (CNNs) have been widely used for light detection and ranging (LiDAR) data classification. Although CNNs achieve good classification performance for LiDAR data classification, a lot of efforts are needed to design a proper architecture. In this letter, the automatic modularized design of CNN is explored for LiDAR data classification for the first time. First, a searchable architecture containing convolution and pooling operations is used to establish the search space. Then, the optimal building block (i.e., cell), which is the basic part of a deep CNN, is obtained from search space by a gradient decent-based algorithm. At last, by stacking several optimal building blocks, a deep CNN can be formulated for LiDAR data classification. Moreover, in order to mitigate the overfitting problem in training a CNN, improved label smoothing and feature regularization are proposed to further improve the classification performance of LiDAR data. The proposed classification models are evaluated on two popular LiDAR data sets (i.e., the Bayview Park and Houston data sets). The experimental results show that the proposed models provide the competitive results compared to the state-of-the-art methods.