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Although the high dimensionality of hyperspectral data increases the separability of land covers, it is difficult to distinguish certain classes using only the spectral information due to the widespread mixed pixels and small sample size problems. Three-dimensional Gabor wavelet transform takes the entire hyperspectral data cube as a tensor, captures the joint spectral-spatial structures very well...
Recently, we have introduced 3-D Gabor wavelets to extract the discriminative features from the hyperspectral imagery for classification. High classification accuracies have been achieved even with small training sample set. However, the computational load of the convolution operator between the original hyperspectral data and the 3-D Gabor wavelet filter is quite high. Furthermore, more than fifty...
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