Abstract
Map databases usually suffer from obsolete scene details due to frequently occurring changes, therefore automatic change detection has become vital. Generally, change detection is done by spectral analysis of multi temporal images without including elevation information. In this paper, we describe a method for urban change detection by fusing high resolution aerial images with airborne lidar data which provides elevation information. For dealing with radiometric differences, three supervised learning algorithms are introduced which reduce the need for radiometric corrections. Three experiments are conducted on different training sets for each algorithm, to evaluate their performance on change detection and their sensitivity to unbalanced and noisy datasets. All algorithms are also compared with the standard PCA method. Experimental results demonstrate the capabilities of these methods and a detailed theoretical analysis of the achieved results is also presented.
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Zong, K., Sowmya, A., Trinder, J. (2013). Machine Learning Based Urban Change Detection by Fusing High Resolution Aerial Images and Lidar Data. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_51
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DOI: https://doi.org/10.1007/978-3-642-45025-9_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45024-2
Online ISBN: 978-3-642-45025-9
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