{"id":11202,"date":"2014-01-09T01:19:41","date_gmt":"2014-01-08T23:19:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=11202"},"modified":"2014-01-09T01:19:41","modified_gmt":"2014-01-08T23:19:41","slug":"real-time-kap-systems-for-image-enhancementreconstruction-of-remote-sensing-imagery","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11202","title":{"rendered":"Real Time KAP Systems for Image Enhancement\/Reconstruction of Remote Sensing Imagery"},"content":{"rendered":"<p>In this study, an implementation of a Kite Aerial Photography (KAP) system for real time image enhancement \/reconstruction of remote sensing (RS) imagery is presented. The system is comprised in three stages: first, a gyro-stabilized mechatronic platform for the image acquisition is developed; second, the multispectral images are transmitted via RF to ground station; and finally, based on the application of parallel computing techniques, the Robust Adaptive Space Filter (RASF) Regularization algorithm is employed using Graphic Processor Units (GPUs). Experimental validation of the presented approach demonstrates the real time processing capabilities of the KAP system for the enhancement\/reconstruction of large-scale remote sensing images.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this study, an implementation of a Kite Aerial Photography (KAP) system for real time image enhancement \/reconstruction of remote sensing (RS) imagery is presented. The system is comprised in three stages: first, a gyro-stabilized mechatronic platform for the image acquisition is developed; second, the multispectral images are transmitted via RF to ground station; and [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,33,3],"tags":[1787,14,1786,20,1232,179],"class_list":["post-11202","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gts-450","tag-sensing"],"views":2577,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11202","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=11202"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11202\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11202"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11202"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11202"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}