{"id":237,"date":"2022-05-01T20:10:11","date_gmt":"2022-05-01T20:10:11","guid":{"rendered":"https:\/\/geospatial-ai.de\/?p=237"},"modified":"2022-05-01T20:10:12","modified_gmt":"2022-05-01T20:10:12","slug":"using-sentinel-1-sar-data-for-imagery-intelligence-detection-of-vessels","status":"publish","type":"post","link":"https:\/\/geospatial-ai.de\/?p=237","title":{"rendered":"Using Sentinel-1 SAR data for Imagery Intelligence &#8211; Detection of vessels"},"content":{"rendered":"\n<p>Sentinel-1 is the first spacecraft in the European Space Agency\u2019s Copernicus Program satellite system. This mission comprises two satellites, Sentinel-1A and Sentinel-1B, which circle in the same orbital plane. They have a C-band SAR (synthetic aperture radar) equipment that collects data in any weather condition, day or night. The satellites offer a maximum spatial resolution of 5 meters and a sweep of up to 400 kilometers. The collected data is easily accessible and a variety of users can freely access the data for public, scientific, or commercial reasons.<\/p>\n\n\n\n<p>The detection of vessels using Sentinel-1 SAR data is a typical domain-specific use-case for complex data science. We need deeper insights of imagery intelligence and the common methods and tools involved. There is also a need for huge training datasets, including access to monitored vessel tracks.<\/p>\n\n\n\n<div class=\"wp-block-button is-style-outline\"><a class=\"wp-block-button__link\" href=\"https:\/\/medium.com\/geospatial-intelligence\/using-sentinel-1-sar-data-for-imagery-intelligence-detection-of-vessels-df8e282ac2c0\">Read more @ Medium<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Sentinel-1 is the first spacecraft in the European Space Agency\u2019s Copernicus Program satellite system. This mission comprises two satellites, Sentinel-1A and Sentinel-1B, which circle in the same orbital plane. They have a C-band SAR (synthetic aperture radar) equipment that collects data in any weather condition, day or night. The satellites offer a maximum spatial resolution &hellip; <\/p>\n","protected":false},"author":1,"featured_media":240,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[11],"class_list":["post-237","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-geoint","tag-geoint"],"_links":{"self":[{"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/posts\/237","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=237"}],"version-history":[{"count":2,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/posts\/237\/revisions"}],"predecessor-version":[{"id":278,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/posts\/237\/revisions\/278"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=\/wp\/v2\/media\/240"}],"wp:attachment":[{"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=237"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=237"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geospatial-ai.de\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=237"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}