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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Cross, James"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Irina Gladkova"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-01T08:00:00Z","date_published":"2014-01-01T08:00:00Z","updated_at":"2026-07-24T01:57:14Z","subjects":["data fusion","remote sensing","image processing","Computer Sciences","Databases and Information Systems","Programming Languages and Compilers"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/647","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Irina Gladkova"]},{"key":"dc:creator","label":"Author","values":["Cross, James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-10-14T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["data fusion","remote sensing","image processing","Computer Sciences","Databases and Information Systems","Programming Languages and Compilers"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/647"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Assessing Satellite Image Data Fusion with Information Theory Metrics"]}]}],"canonical_facts":{"dc:contributor":["Irina Gladkova"],"dc:creator":["Cross, James"],"dc:date.available":["2016-10-14T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/647"],"dc:subject":["data fusion","remote sensing","image processing","Computer Sciences","Databases and Information Systems","Programming Languages and Compilers"],"dc:title":["Assessing Satellite Image Data Fusion with Information Theory Metrics"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:57:14Z"}