Semi-Supervised Pyramid Feature Co-Training Network for Lidar Data Classification

Zexin Wang, Haoran Wang, Licheng Jiao, Xu Liu

Abstract: In the light detection and ranging (LiDAR) data classification, there are two problems affecting the LiDAR data classification performance. One is limited labeled samples in LiDAR dataset, another is confusing categories with extremely similar appearances. To address these challenges, this paper proposes a novel semi-supervised extended label algorithm (SSELA) for the data expansion, based on a novel pyramid feature co-training network (PFCTN). The proposed approach characterizes each level of feature pyramids without increasing the computational burden and time consumption, which has a benefit for learning complementary features of confusing categories. Experimental results demonstrate that the PFCTN alleviates the decreasing accuracy phenomenon at different scales of similar scenes, and achieves the promising results with limited labeled LiDAR dataset.