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

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3 Author(s)
Gormus, E.T. ; Dept. of Electr. & Electron. Eng., Univ. of Bristol, Bristol, UK ; Canagarajah, N. ; Achim, A.

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.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Date of Conference:

22-27 July 2012