Talks and Poster Presentations (with Proceedings-Entry):
N. Li, C. Liu, N. Pfeifer, J. Yin, Z. Liao, Y. Zhou:
"Tensor Modeling Based for Airborne LiDAR Data Classification";
Poster: XXIII ISPRS Congress,
Prague, Czechia;
2016-07-12
- 2016-07-19; in: "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,
XLI-B3
(2016),
ISSN: 1682-1750;
283
- 287.
English 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.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.5194/isprs-archives-XLI-B3-283-2016
Electronic version of the publication:
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/283/2016/isprs-archives-XLI-B3-283-2016.pdf
Created from the Publication Database of the Vienna University of Technology.