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Proceedings Paper

Aerial lidar data classification using weighted support vector machines
Author(s): Ning Guo; Gang Xu
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Paper Abstract

This paper presents our research on classifying scattered 3D aerial Lidar height data into ground, vegetable (trees) and man-made object (buildings) using Support Vector Machine algorithm. To this end, the most basic theory of SVM is first outlined and with concern to the fact that features are differed in their contribution to classification, Weighted Support Vector Machine (W-SVM) technique is proposed. Second, four features consist of height, height variation, plane fitting error and Lidar return intensity are identified for classification purposes. In this step, features are normalized respectively and their weight that indicates feature's contribution to certain class or multi-class as a whole are calculated and specified. Third, Based on W-SVM technique, one 1AAA1 solution to multi-class classification is proposed by integration "one against one" and "one against all" solution together. Finally, the classification results of LIDAR data with presented technique clearly demonstrate higher classification accuracy and valuable conclusions are given as well.

Paper Details

Date Published: 9 July 2011
PDF: 7 pages
Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 800926 (9 July 2011); doi: 10.1117/12.896198
Show Author Affiliations
Ning Guo, Guilin Univ. of Electronic Technology (China)
Gang Xu, Guilin Univ. of Electronic Technology (China)


Published in SPIE Proceedings Vol. 8009:
Third International Conference on Digital Image Processing (ICDIP 2011)
Ting Zhang, Editor(s)

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