In order to reduce high dimensions of hyperspectral remote sensing image and concentrate optimal information to
reduced bands, this paper proposed a new method of feature extraction. The new method has two steps. The first step is
to reduce the high dimensions by selecting high informative and low correlative bands according to the indexes
calculated by a smart band selection method. The criterions that SBS method complied are: (1) The selected bands have
the most information; (2) The selected bands have the smallest correlation with other bands. The second step is to
decompose the selected bands by a novel second generation wavelet, predicting and updating subimages on rectangle
and quincunx grids by Neville filters, finally using variance weighting as fusion weight. A 126-band HYMAP
hyperspectral data was experimented in order to test the effect of the new method. The results showed classification
accuracy is increased by using the novel feature extraction method.
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