Noise removal in multichannel image data by a parametric maximum noise fraction estimator

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    Abstract

    Some approaches to noise removal in multispectral imagery are presented. The primary contribution of the present work is the establishment of several ways of estimating the noise covariance matrix from image data and a comparison of the noise separation performances. A case study with Landsat MSS data demonstrates that the principal components are not sorted correctly in terms of visual image quality, whereas the minimum/maximum autocorrelation factors and the maximum noise fractions (MAFs) are. A case study with Landsat TM data shows an ordering which is consistent with the spatial wavelength in the components. The case studies indicate that a better noise separation is attained when using more complex noise models than the simple model implied by MAF analysis. (L.M.)
    Original languageEnglish
    Title of host publication24th Symposium on Remote Sensing of Environment, Rio de Janeiro, Brazil
    Number of pages14
    Publication date1991
    Publication statusPublished - 1991
    Event24th Symposium on Remote Sensing of Environment, - Rio de Janeiro, Brazil
    Duration: 1 Jan 1991 → …

    Conference

    Conference24th Symposium on Remote Sensing of Environment,
    CityRio de Janeiro, Brazil
    Period01/01/1991 → …

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