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The common statistical methods for supervised classification usually require a large amount of training data to\udachieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse\udrepresentation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors\udto keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse\udtensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction\udresiduals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished\udby this algorithm successfully

AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION