A Comparison Of Noise Reduction Methods For Image Enhancement In Classification Of Hyperspectral Imagery

A particular challenge in hyperspectral remote sensing of benthic habitats is that the signal exiting from the water is a small component of the overall signal received at the satellite or airborne sensor. Therefore, in order to be able to discriminate different ecological areas in benthic habitats, it is important to have a high signal to noise ratio (SNR). The SNR can be improved by building better sensors; SNR improvements however, we believe, are also achievable by means of signal processing and by taking advantage of the unique characteristics of hyperspectral sensors. One approach for SNR improvement is based on signal oversampling. Another approach for SNR improvement is Reduced Rank Filtering (RRF) where the small Singular Values of the image are discarded and then reconstruct a lower rank approximation to the original image. This paper presents a comparison in the use of oversampling filtering (OF) versus RRF as SNR enhancement methods in terms of classification accuracy and class separability when used as a preprocessing step in a classification system. Overall results show that OF does a better job improving the classification accuracy than RRF and at much lower computational cost, making it an attractive technique for Hyperspectral Image Processing.