Assessing Satellite Image Data Fusion with Information Theory Metrics

<p>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.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2014

Author dc:creator

• Cross, James

Contributors dc:contributor

• Irina Gladkova

• data fusion
• remote sensing
• image processing
• Computer Sciences
• Databases and Information Systems
• Programming Languages and Compilers

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/647

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1647