Satellite image classification using a classifier integration model

  • Authors:

  • Dong-Chul Park;Taekyung Jeong;Yunsik Lee;Soo-Young Min

  • Affiliations:

  • Dept. of Electronics Engineering, Myong Ji University, Yong-In, Korea;Dept. of Electronics Engineering, Myong Ji University, Yong-In, Korea;System IC R&D Division, Korea Electronics Tech. Inst., Songnam, Korea;System IC R&D Division, Korea Electronics Tech. Inst., Songnam, Korea

  • Venue:
  • AICCSA '11 Proceedings of the 2011 9th IEEE/ACS International Conference on Computer Systems and Applications
  • Year:
  • 2011

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Abstract

A new satellite image classification method using a classifier integration model(CIM)is proposed in this paper. CIM does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The CIM is applied to the problem of satellite image classification on a set of image data. The results demonstrate that the CIM scheme can enhance the classification accuracy of individual classifiers that use specific feature vector group.