The spectrum of most objects in a hyperspectral image is oversampled in the spectral dimension due to the images having many closely spaced spectral samples. This oversampling implies that there is redundant information in the image which can be exploited to reduce the noise, and so increase the correct classification percentage. Oversampling techniques have been shown to be useful in the classification of hyperspectral imagery. These previous techniques consist of a lowpass filter in the spectral dimension whose characteristics are chosen based on the average spectral density of many objects to be classified. A better way of selecting the characteristics of the filter is to calculate the spectral density and oversampling of each object, and use that to determine the filter. The algorithm proposed here exploits the fact that the system is supervised to determine the oversampling rate, using the training samples for this purpose. The oversampling rate is used to determine the cutoff frequency for each class, and the highest of these is used to filter the whole image. Two pass approaches, where each class in the image is filtered with its own filter, were studied, but the increase in performance did not justify the increase in computational load. The results of applying these techniques to data to be classified are presented. The results, using AVIRIS imagery, show a significant improvement in classification performance.