Hierarchical extraction of remote sensing data based on support vector machines and knowledge processing

Authors:
Chao-feng Li;Lei Xu;Shi-tong Wang
Affiliations:
School of Information Technology, Southern Yangtze University, Wuxi, China;School of Information Technology, Southern Yangtze University, Wuxi, China;School of Information Technology, Southern Yangtze University, Wuxi, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 3
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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.