Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 107-114, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 107-114, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

AIRBORNE LIDAR POINTS CLASSIFICATION BASED ON TENSOR SPARSE REPRESENTATION

N. Li1,2, N. Pfeifer2, and C. Liu1 N. Li et al.
  • 1College of Survey and Geoinformation, Tongji University, 200092, Shanghai, China
  • 2Deptartment of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, Austria

Keywords: Point cloud, Sparse coding, Dictionary Learning

Abstract. 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.