Volume XL-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 191-196, 2014
https://doi.org/10.5194/isprsarchives-XL-2-191-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 191-196, 2014
https://doi.org/10.5194/isprsarchives-XL-2-191-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Nov 2014

11 Nov 2014

Application of ECOC SVMS in Remote Sensing Image Classification

Z. Yan1,2 and Y. Yang1,2 Z. Yan and Y. Yang
  • 1School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, Jiangsu, 221116 P.R.China
  • 2Jiangsu Key Laboratory of Resources & Environmental Information Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 22116 P.R.China

Keywords: Remote Sensing Classification, ECOC-SVMs, Multi-class Classifier

Abstract. Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.