Hierarchical Extraction of Remote Sensing Data Based on Support Vector Machines and Knowledge Processing
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.
KeywordsSupport Vector Machine Remote Sensing Support Vector Machine Model Remote Sensing Data Road Information
Unable to display preview. Download preview PDF.
- Paola, J.D., Schowengerdt, R.A.: A Review and Analysis of Back-propagation Neural Networks for Classification of Remotely Sensed Multi-spectral Image. Int. J. GIS 16(16), 3033–3058 (1995)Google Scholar
- Li, C.F., Wang, Z.Y.: Remote Sensing Image Classification Method Based on Support Vector Machines and Fuzzy Membership Function. In: MIPPR 2005: SAR and Multispectral Image Processing, vol. 6043, p. 604324-1–7. SPIE, Wuhan (2005)Google Scholar
- Palacio-prieto, J.L.: Improving Spectral Result in a GIS Context. Int. J. GIS 17(11), 2201–2209 (1996)Google Scholar