Assessing Satellite Image Data Fusion with Information Theory Metrics

Abstract

A common problem in remote sensing is estimating an image with high spatial and high spectral resolution given separate sources of measurements from satellite instruments, one having each of these desirable properties. This thesis presents a survey of seven families of algorithms which have been developed to provide this common pattern of satellite image data fusion. They are all tested on artificially degraded sets of satellite data from the Moderate Resolution Imaging Spectroradiometer (“MODIS”) with known ideal results, and evaluated using the commonly accepted data fusion assessment metrics spectral angle mapper (“SAM”) and Erreur Relative Globale Adimensionelle de Synth`ese (“ERGAS”). It is also established that the information theory metric mutual information can predict the performance of certain data fusion algorithms (pan-sharpening, principal component analysis (“PCA”) based, and high-pass filter (“HPF”) based) but not others