ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 135-140, 2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
13 Jul 2012
S. Ghosh and B. Lohan Geoinformatics Laboratory, Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, 208 016 India
Keywords: LiDAR, feature extraction, visualization, statistics, non-parametric Abstract. LiDAR (Light Detection and Ranging) has attained the status of an industry standard method of data collection for gathering three dimensional topographic information. Datasets captured through LiDAR are dense, redundant and are perceivable from multiple directions, which is unlike other geospatial datasets collected through conventional methods. This three dimensional information has triggered an interest in the scientific community to develop methods for visualizing LiDAR datasets and value added products. Elementary schemes of visualization use point clouds with intensity or colour, triangulation and tetrahedralization based terrain models draped with texture. Newer methods use feature extraction either through the process of classification or segmentation. In this paper, the authors have conducted a visualization experience survey where 60 participants respond to a questionnaire. The questionnaire poses six di erent questions on the qualities of feature perception and depth for 12 visualization schemes. The answers to these questions are obtained on a scale of 1 to 10. Results are thus presented using the non-parametric Friedman's test, using post-hoc analysis for hypothetically ranking the visualization schemes based on the rating received and finally confirming the rankings through the Page's trend test. Results show that a heuristic based visualization scheme, which has been developed by Ghosh and Lohani (2011) performs the best in terms of feature and depth perception.
Conference paper (PDF, 11537 KB)

Citation: Ghosh, S. and Lohan, B.: EXPERIMENTAL EVALUATION OF LIDAR DATA VISUALIZATION SCHEMES, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-2, 135-140, doi:10.5194/isprsannals-I-2-135-2012, 2012.

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