Methods for LiDAR point cloud classification using local neighborhood statistics
Kim, Angela M.
Olsen, Richard C.
Kruse, Fred A.
MetadataShow full item record
LiDAR data are available in a variety of publicly-accessible forums, providing high-resolution, accurate 3- dimensional information about objects at the Earth’s surface. Automatic extraction of information from LiDAR point clouds, however, remains a challenging problem. The focus of this research is to develop methods for point cloud classification and object detection which can be customized for specific applications. The methods presented rely on analysis of statistics of local neighborhoods of LiDAR points. A multi-dimensional vector composed of these statistics can be classified using traditional data classification routines. Local neighborhood statistics are defined, and examples are given of the methods for specific applications such as building extraction and vegetation classification. Results indicate the feasibility of the local neighborhood statistics approach and provide a framework for the design of customized classification or object detection routines for LiDAR point clouds.
The article of record as published may be found at http://dx.doi.org/10.1117/12.2015709
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Showing items related by title, author, creator and subject.
Muller, Cole (Monterey, California. Naval Postgraduate School, 2003-06);The J-52 engine used in the EA-6B Prowler has been found to have a faulty design which has led to in-flight engine failures due to the degradation of the 4.5 roller bearing. Because of cost constraints, the Navy developed ...
Kennedy, Sean M. (Monterey, CA; Naval Postgraduate School, 2019-09);In this thesis, a three-stage algorithm for performing unsupervised segmentation of hyperspectral imagery is developed and tested. Based on a data-model derived from common sources of error inherent in all imaging ...
Fargues, Monique P. (Monterey, California. Naval Postgraduate School, 2001); NPS-EC-01-005Extracting relevant features that allow for class discrimination is the first critical step in classification applications. However, this step often leads to high-dimensional feature spaces, which requires large datasets ...