Morillo-Contreras, Shirley

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    A comparison of resolution enhancement methods as pre-processing for classification of hyperspectral images
    (2004) Morillo-Contreras, Shirley; Vélez-Reyes, Miguel; College of Engineering; Hunt, Shawn; Rodriguez, Manuel; Department of Electrical and Computer Engineering; Castillo, Paul
    The increasing use of Hyperspectral data is causing many data analysis problems; one of these problems is to reduce noise in Hyperspectral images. One approach is resolution enhancement technique based on oversampling theory. The oversampled spectrum in a Hyperspectral image implies that the information is redundant which can be exploited to reduce noise. Another approach is Truncated Singular Value Decomposition (TSVD), a method for noise reduction. The main idea of this method is to let the Hyperspectral image represent the noisy signal, compute the Singular Values Decomposition, discard small singular values that represent the noise, and then reconstruct the filtered image. This research work compares the use of resolution enhancement versus TSVD filtering as image enhancement pre-processor on classification accuracy and class separability of Hyperspectral imagery. Hyperspectral imagery from different sensors showing different scenarios were use for the study. Overall results show that resolution enhancement pre-processing does a better job improving the classification accuracy than TSVD and at much lower computational cost, making it an attractive technique for Hyperspectral Image Processing.