Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification
Abstract: To effectively learn heterogeneous features extracted from raw LiDAR point cloud data for landcover classification, a multiple kernel sparse representation classification (MKSRC) framework is proposed in this paper. In the MKSRC, multiple kernel learning (MKL) is embedded into sparse representation classification (SRC). The heterogeneous features are fi first rst extracted from the raw LiDAR point cloud data before classification. These features contain useful information from different dimensions, including single point features and neighbor features. Based on feature extraction, on the one hand, MKL is reasonably ably integrated into the SRC, namely, different base kernels that are constructed with each heterogeneous feature separately are utilized in the process of sparse representation. Furthermore, joint sparsity model is also introduced into the MKSRC framework and multiple kernel joint SRC (MKJSRC) is then proposed. On the other hand, improved kernel alignment (IKA) methods are proposed to more effectively determine the weights of base kernels in both of MKSRC and MKJSRC. Experiments are conducted on three real airborne LiDAR data sets. The experimental results demonstrate that MKSRC and MKJSRC frameworks can effectively learn the heterogeneous features for LiDAR point cloud classification and outperforms the other state state-of-the the-art sparse representation-based classifiers assifiers and the recent MKL algorithm. Moreover, the