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28 April 2010 A linked feature space approach to exploring lidar data
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Abstract
A typical approach to exploring Light Detection and Ranging (LIDAR) datasets is to extract features using pre-defined segmentation algorithms. However, this approach only provides a limited set of features that users can investigate. To expand and represent the rich information inside the LIDAR data, we introduce a linked feature space concept that allows users to make regular, conjunctive, and disjunctive discoveries in non-uniform LIDAR data by interacting with multidimensional transfer functions. We achieve this by providing interactions for creating multiple scatter-plots of varying axes, establishing chains of plots based on selection domains, linking plots using logical operators, and viewing selected brushing results in both a 3D view and selected scatter-plots. Our highly interactive approach to visualizing LIDAR feature spaces facilitates the users' ability to explore, identify, and understand data features in a novel way. Our approach for exploring LIDAR data can directly lead to better understanding of historical LIDAR datasets, and increase the turnaround time and quality of results from time-critical LIDAR collections after urban disasters or on the battlefield.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lane Harrison, Thomas Butkiewicz, Xiaoyu Wang, William Ribarsky, and Remco Chang "A linked feature space approach to exploring lidar data", Proc. SPIE 7709, Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II, 77090X (28 April 2010); https://doi.org/10.1117/12.850579
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