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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.
|Title of host publication||International Geoscience and Remote Sensing Symposium (IGARSS)|
|Number of pages||4|
|Publication status||Published - 1 Dec 2012|
|Event||2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, United Kingdom|
Duration: 22 Jul 2012 → 27 Jul 2012
|Conference||2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012|
|Period||22/07/12 → 27/07/12|
- Decision Fusion
- Dimensionality Reduction
- Discrete Wavelet Transform
- Empirical Mode Decomposition
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1/06/09 → 1/04/12