Use of High Dimensional Model Representation in Dimensionality Reduction: Application to Hyperspectral Image Classification

Taşkın Kaya G.

22nd SPIE Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, Maryland, United States Of America, 18 - 21 April 2016, vol.9840 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 9840
  • Doi Number: 10.1117/12.2227326
  • City: Maryland
  • Country: United States Of America
  • Istanbul Technical University Affiliated: Yes


Recently, information extraction from hyperspectral images (HI) has become an attractive research area for many practical applications in earth observation due to the fact that HI provides valuable information with a huge number of spectral bands. In order to process such a huge amount of data in an effective way, traditional methods may not fully provide a satisfactory performance because they do not mostly consider high dimensionality of the data which causes curse of dimensionality also known as Hughes phenomena. In case of supervised classification, a poor generalization performance is achieved as a consequence resulting in availability of limited training samples. Therefore, advance methods accounting for the high dimensionality need to be developed in order to get a good generalization capability. In this work, a method of High Dimensional Model Representation (HDMR) was utilized for dimensionality reduction, and a novel feature selection method was introduced based on global sensitivity analysis. Several implementations were conducted with hyperspectral images in comparison to state of -art feature selection algorithms in terms of classification accuracy, and the results showed that the proposed method outperforms the other feature selection methods even with all considered classifiers, that are support vector machines, Bayes, and decision tree j48.