A NOVEL DECISION FUSION APPROACH TO IMPROVING CLASSIFICATION ACCURACY OF HYPERSPECTRAL IMAGES


Gormus E. , Canagarajah N., Achim A.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22 - 27 July 2012, pp.4158-4161 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/igarss.2012.6351696
  • City: Munich
  • Country: Germany
  • Page Numbers: pp.4158-4161

Abstract

In this paper discrete wavelet transform (DWT) and empirical mode decomposition (EMD) are employed as a preprocessing stage in a multiclassifier and decision fusion system. The proposed method consists of three steps. In the first step, 2D-EMD is performed on each hyperspectral image band in order to obtain useful spatial information. Then, useful spectral information is obtained by applying the 1D-DWT to each signature of 2D-EMD performed bands. A novel feature set is generated using both spectral and spatial information. In the second step, each feature is independently classified by support vector machines (SVM), creating a multiclassifier system. In the last step, classification results are fused using a decision fusion criterion to produce one final classification. The proposed method improves overall classification accuracy over independent classifiers when reduced number of features are employed.