{"id":881361,"date":"2022-09-27T18:49:15","date_gmt":"2022-09-28T01:49:15","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-09-27T18:49:15","modified_gmt":"2022-09-28T01:49:15","slug":"location-aware-super-resolution-for-satellite-data-fusion","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/location-aware-super-resolution-for-satellite-data-fusion\/","title":{"rendered":"Location Aware Super-Resolution for Satellite Data Fusion"},"content":{"rendered":"<p><span dir=\"ltr\" role=\"presentation\">Satellite data fusion involves images with different spatial, temporal, <\/span><span dir=\"ltr\" role=\"presentation\">and spectral resolution.<\/span> <span dir=\"ltr\" role=\"presentation\">These images are taken <\/span><span dir=\"ltr\" role=\"presentation\">under different illumination conditions, with different sensors <\/span><span dir=\"ltr\" role=\"presentation\">and atmospheric noise. We use classic super-resolution algo<\/span><span dir=\"ltr\" role=\"presentation\">rithms to synthesize commercial satellite images <\/span><span dir=\"ltr\" role=\"presentation\">from a public satellite source (Sentinel-2).<\/span> <span dir=\"ltr\" role=\"presentation\">Each super-resolution <\/span><span dir=\"ltr\" role=\"presentation\">resolution method is then further improved by adaptive sharp<\/span><span dir=\"ltr\" role=\"presentation\">ening to the location by use of matrix completion (regression <\/span><span dir=\"ltr\" role=\"presentation\">with missing pixels). Finally, we consider ensemble systems <\/span><span dir=\"ltr\" role=\"presentation\">and a residual channel attention dual network with stochastic <\/span><span dir=\"ltr\" role=\"presentation\">dropout.<\/span> <span dir=\"ltr\" role=\"presentation\">The resulting systems are visibly less blurry with <\/span><span dir=\"ltr\" role=\"presentation\">higher fidelity and yield improved performance<\/span><\/p>\n<div id=\"attachment_881364\" style=\"width: 310px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-881364\" class=\"size-medium wp-image-881364\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/09\/collage-300x186.jpg\" alt=\"Location Aware Super Resolution\" width=\"300\" height=\"186\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/09\/collage-300x186.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/09\/collage-768x477.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/09\/collage-240x149.jpg 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2022\/09\/collage.jpg 856w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><p id=\"caption-attachment-881364\" class=\"wp-caption-text\">Example of several super-resolution methods from 6 locations. The standard super-resolution methods are notably blurrier compared to the ground truth in column 1. The ensembles combine the 3 preceding columns using SRCNN.<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Satellite data fusion involves images with different spatial, temporal, and spectral resolution. These images are taken under different illumination conditions, with different sensors and atmospheric noise. We use classic super-resolution algorithms to synthesize commercial satellite images from a public satellite source (Sentinel-2). Each super-resolution resolution method is then further improved by adaptive sharpening to the [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"IEEE","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"IGARSS 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