A novel decision fusion approach to improving classification accuracy of hyperspectral images

Esra Tunc Gormus*, Nishan Canagarajah, Alin Achim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
Pages4158-4161
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2012
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, United Kingdom
Duration: 22 Jul 201227 Jul 2012

Conference

Conference2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryUnited Kingdom
CityMunich
Period22/07/1227/07/12

Keywords

  • Classification
  • Decision Fusion
  • Dimensionality Reduction
  • Discrete Wavelet Transform
  • Empirical Mode Decomposition

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