Although spatial and spectral resolutions of remotely sensed data have been improved, the usage of multispectral imagery is not sufficient for urban feature classification. This article addresses the problem of automated classification by integrating airborne lidar range data and aerial
imagery. In this study, the classification procedure is divided into three phases. We first use the lidar range data to obtain the coarse lidar-based classification results, by which a lidar-driven labelled image and a lidar-driven high-rise object mask are acquired in this phase. Then, at
the image-based classification level, we train samples based on the lidar-driven labelled image and conduct maximum likelihood classification experience with the lidar-driven normalized digital surface model as a high-rise object mask. Finally, we propose a knowledge-based cross-validation
(KBCV) for misclassification between the lidar-based classification results and the image-based classification results. Experimental results are presented to demonstrate the benefits of the training sample selection of the lidar-driven labelled image, using the lidar-driven high-rise object
mask, and the greater classification accuracy of the KBCV.
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Document Type: Research Article
Department of Geography and Environmental Management,University of Waterloo, Waterloo,Ontario, CanadaN2L 3G1,
School of Remote Sensing Information and Engineering,Wuhan University, Wuhan,430079, China
Changjiang Spatial Information Technology Engineering Company,Changjiang Institute of Survey Planning Design and Research, Wuhan,430010, China
Publication date: January 10, 2013
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