Next Article in Journal
Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods
Previous Article in Journal
Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1216;

Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points

Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria
College of Survey and Geoinformation, Tongji University, 200092 Shanghai, China
Author to whom correspondence should be addressed.
Received: 22 October 2017 / Revised: 17 November 2017 / Accepted: 24 November 2017 / Published: 27 November 2017
Full-Text   |   PDF [7498 KB, uploaded 29 November 2017]   |  


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. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC) method for airborne LiDAR (Light Detection and Ranging) points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit) algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers. View Full-Text
Keywords: tenor sparse coding; structured and discriminative dictionary learning; feature extraction tenor sparse coding; structured and discriminative dictionary learning; feature extraction

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Li, N.; Pfeifer, N.; Liu, C. Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points. Remote Sens. 2017, 9, 1216.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top