Lidar Data Classification Algorithm Based on Generative Adversarial Network

In this paper, the Generative Adversarial Network (GAN) is applied to LiDAR data classification. Generative Adversarial Network usually includes a generating network and a discriminant network. In GAN, a convolutional neural network (CNN) is designed to distinguish inputs. Another CNN is used to generate so-called false inputs. Combining with the actual training samples, the discriminant CNN is fine-tuned to improve the final classification performance. The proposed classifier is implemented on real data sets. The results show that the accuracy of the proposed network is higher than that of the classification method based on CNN, which shows that the features extracted by the network have better discrimination and stronger competitiveness.