Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysisWei, H. and Bartels, M. (2006) Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis. In: Tang, Y. Y., Wang, S. P., Lorette, G., Yeung, D. S. and Yan, H. (eds.) 18th International Conference on Pattern Recognition, Vol 1, Proceedings. International Conference on Pattern Recognition. IEEE Computer Soc, Los Alamitos, pp. 667-670. ISBN 1051-4651 0-7695-2521-0 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryIn this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.
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