Joint Discriminative Dictionary and Classifier Learning for ALS Point Cloud Classification

Abstract

To efficiently recognize on-ground objects in airborne laser scanning (ALS) point clouds, we design a method that jointly learns a discriminative dictionary and a classifier. In the method, the point cloud is segmented into hierarchical point clusters, which are organized by a tree structure. Then, the feature of each point cluster is extracted. The feature of a leaf node is obtained by aggregating the features of all its parent nodes. The feature of the leaf node is called the hierarchical aggregation feature. The hierarchical aggregation features are encoded by sparse coding. We introduce a new label consistency constraint called “discriminative sparse-code error,” and combine it with the reconstruction error, the classification error, and $L_{1}$ -norm sparsity constraint to form a unified objective function. The objective function is efficiently solved by using the proposed label consistency feature sign method. We obtain an overcomplete discriminative dictionary and an optimal linear classifier. Experiments performed on different ALS point cloud scenes have shown that the hierarchical aggregation features combined with the learned classifier can significantly enhance the classification results, and also demonstrated the superior performance of our method over other techniques in point cloud classification.