Abstract
A new extraction method for remote sensing data is proposed by using both a support vector machine (SVM) and knowledge reasoning technique. The new method fulfils intelligent extraction of water, road and other plane-like objects from remote sensing images in a hierarchical manner. It firstly extracts water and road information by a SVM and pixel-based knowledge post-processing method, then removes them from original image, and then segments other plane-like objects using the SVM model and computes their features such as texture, elevation, slope, shape etc., finally extracts them by the polygon-based uncertain reasoning method. Experimental results indicate that the new method outperforms the single SVM and moreover avoids the complexity of single knowledge reasoning technique.
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, Cf., Xu, L., Wang, St. (2006). Hierarchical Extraction of Remote Sensing Data Based on Support Vector Machines and Knowledge Processing. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_68
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DOI: https://doi.org/10.1007/11760023_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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