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dc.contributor.authorKim, Angela M.
dc.contributor.authorOlsen, Richard C.
dc.contributor.authorKruse, Fred A.
dc.date.accessioned2016-03-02T20:54:55Z
dc.date.available2016-03-02T20:54:55Z
dc.date.issued2013
dc.identifier.citationProc. of SPIE Vol. 8731 873103-1, 10 p.en_US
dc.identifier.urihttp://hdl.handle.net/10945/48061
dc.descriptionThe article of record as published may be found at http://dx.doi.org/10.1117/12.2015709en_US
dc.description.abstractLiDAR 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.en_US
dc.format.extent10 p.en_US
dc.publisherSPIEen_US
dc.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.en_US
dc.titleMethods for LiDAR point cloud classification using local neighborhood statisticsen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate Schoolen_US
dc.contributor.departmentRemote Sensing Center and Physicsen_US
dc.subject.authorLiDARen_US
dc.subject.authorpoint clouden_US
dc.subject.authorclassificationen_US
dc.subject.authorstatisticsen_US
dc.subject.authorlocal neighborhooden_US


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