Local Averaging Based Feature Extraction on Hyperspectral Image Data

Gokdag U., BİLGİN G.

17th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, 17 - 19 November 2016, pp.157-161 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/cinti.2016.7846396
  • City: Budapest
  • Country: Hungary
  • Page Numbers: pp.157-161


This paper focuses on the land cover/usage area classification problem by using local averaging for feature extraction method. In hyperspectral image classification tasks, spatial information is also useful as much as spectral information. A pipeline of methods is utilized using Fisher's discriminant analysis for dimension reduction, z-score value for central limiting and support vector machines and extreme learning machines for classification. The classification accuracies on transformed data set are outperforming previous works by achieving % 99.51 success ratio on for support vector machines and % 99.73 for extreme learning machines on 10-fold cross validation. the proposed method increases classification accuracy significantly while reducing the dimension of the original data by % 95.