LiDAR point classification based on joint sparse representation in kernel space

Bingqian Xie, Yanfeng Gu, Qingwang Wang, Huan Liu

Abstract: Resent years, sparse representation theory has been widely used in signal processing field. Researchers introduce this theory into the application of pattern recognition and classification and get the sparse representation classifier (SRC). In this paper, we use the SRC to achieve the classification of LiDAR (Light Detection and Ranging) points. To get a better performance, we introduce the kernel method into SRC, for the advancement of kernel in solving nonlinear problem. Also, a joint sparse representation is used for the category similarity of neighboring LiDAR points.