Skip to main content
Volume Component Analysis for Classification of LiDAR Data
Proceedings of SPIE 9477, Optical Pattern Recognition XXVI
  • Nina M. Varney, University of Dayton
  • Vijayan K. Asari, University of Dayton
Document Type
Conference Paper
Publication Date

One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach.

This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block.

This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%.

Inclusive pages
94770F-1 to 94770F-6
Document Version
Published Version

This document is provided for download in compliance with the publisher's policy on self-archiving. Permission documentation is on file.

Society of Photo-optical Instrumentation Engineers
Place of Publication
Baltimore, MD
Peer Reviewed
Citation Information
Nina M. Varney and Vijayan K. Asari. "Volume Component Analysis for Classification of LiDAR Data" Proceedings of SPIE 9477, Optical Pattern Recognition XXVI Vol. 9477 (2015)
Available at: