AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION

Li, N.; Pfeifer, N.; Liu, C.

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

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Li, N. / Pfeifer, N. / Liu, C.: AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION. 2017. Copernicus Publications.

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