The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 283–287, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-283-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 283–287, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-283-2016

  09 Jun 2016

09 Jun 2016

TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

N. Li1,2, C. Liu1, N. Pfeifer2, J. F. Yin3, Z.Y. Liao3, and Y. Zhou1 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
  • 3Department of Mathmetrics, Tongji University, 200092, Shanghai, China

Keywords: Feature Selection, Tensor Processing, KNN classification

Abstract. Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the “raw” data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.