Hierarchical Extraction of Remote Sensing Data Based on Support Vector Machines and Knowledge Processing

  • Chao-feng Li
  • Lei Xu
  • Shi-tong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


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.


Support Vector Machine Remote Sensing Support Vector Machine Model Remote Sensing Data Road Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chao-feng Li
    • 1
  • Lei Xu
    • 1
  • Shi-tong Wang
    • 1
  1. 1.School of Information TechnologySouthern Yangtze UniversityWuxiChina

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