Item talk:Q55222: Difference between revisions
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"abstract": "Executive SummaryThis report outlines quality assurance (QA) processes, including radiometric and geometric calibration guidelines, and guidelines for data acquisition and quality control to be followed by U.S. Geological Survey (USGS) researchers for acquiring and processing uncrewed aircraft systems (UAS) data. These QA processes ensure that UAS data can be used for quantitative analysis and are comparable with other standard geospatial data.Remote sensing data play a critical role in monitoring Earth\u2019s resources. Traditionally, the USGS and Department of the Interior have used well calibrated metric sensors mounted on satellite or aircraft platforms to collect these data. These sensors and platforms are stable, and data have been processed using standard pipelines. These processes ensured that the data are generally consistent with each other and benefitted a diverse group of users. These data are shared among multiple researchers around the world through the internet and other means, using standard formats and metadata.In the last few years, UAS platforms have democratized the remote sensing data collection further, bringing an unparalleled level of control of time, sensors, and processes to individual researchers. Together with the development of cheaper and lighter sensors and relaxation of prohibitions against UAS operation in the National Airspace System by the Federal Aviation Authority, researchers can collect remote sensing data using UAS platforms. Researchers often customize the sensors on these UAS systems based on their specific requirements and use ad hoc processing steps to generate data.A challenge is that these data are often produced by a wide array of sensors and processes that render them potentially inconsistent with each other. Therefore, unlike data collected from metric sensors, UAS-based data are designed to benefit only specific user groups. The data thus generated often lack traceability to known standards, making them difficult to use with other geospatial data.This report provides radiometric and geometric calibration guidelines, as well as guidelines for data acquisition and quality control, that can be followed by USGS researchers in acquiring and processing UAS data. Instead of calibrating sensors, researchers collecting UAS data can focus on calibrating the data. Various radiometric calibration processes are provided, and the two panel empirical line method is highlighted for radiometric calibration.For geometric calibration, USGS and Department of the Interior researchers are experienced in following standard calibration procedures provided by standard UAS data processing software. However, researchers may be aided in paying attention to the tie points and the accuracy of the ground control points used for geometric calibration and data production. The accuracy of ground control points are related to the requirements of the project. The ground control points and the quality of tie points directly contribute to the geometric accuracy of the data, regardless of the ground sample distance of the imagery.The guidelines outlined in this report are intended to ensure that the data are in common units and are quantifiable and comparable with other data. These QA and calibration processes can be critical in ensuring that these datasets are used to the maximum extent possible. Including the calibration parameters (and their uncertainties) as part of metadata can allow for easier data discovery and analytical filters.", | |||
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Latest revision as of 22:38, 14 August 2024
{
"USGS Publications Warehouse": { "schema": { "@context": "https://schema.org", "@type": "CreativeWork", "additionalType": "USGS Numbered Series", "name": "Guidelines for calibration of uncrewed aircraft systems imagery", "identifier": [ { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "ofr20231033", "url": "https://pubs.usgs.gov/publication/ofr20231033" }, { "@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70247098 }, { "@type": "PropertyValue", "propertyID": "DOI", "value": "10.3133/ofr20231033", "url": "https://doi.org/10.3133/ofr20231033" } ], "inLanguage": "en", "isPartOf": [ { "@type": "CreativeWorkSeries", "name": "Open-File Report" } ], "datePublished": "2023", "dateModified": "2023-07-25", "abstract": "Executive SummaryThis report outlines quality assurance (QA) processes, including radiometric and geometric calibration guidelines, and guidelines for data acquisition and quality control to be followed by U.S. Geological Survey (USGS) researchers for acquiring and processing uncrewed aircraft systems (UAS) data. These QA processes ensure that UAS data can be used for quantitative analysis and are comparable with other standard geospatial data.Remote sensing data play a critical role in monitoring Earth\u2019s resources. Traditionally, the USGS and Department of the Interior have used well calibrated metric sensors mounted on satellite or aircraft platforms to collect these data. These sensors and platforms are stable, and data have been processed using standard pipelines. These processes ensured that the data are generally consistent with each other and benefitted a diverse group of users. These data are shared among multiple researchers around the world through the internet and other means, using standard formats and metadata.In the last few years, UAS platforms have democratized the remote sensing data collection further, bringing an unparalleled level of control of time, sensors, and processes to individual researchers. Together with the development of cheaper and lighter sensors and relaxation of prohibitions against UAS operation in the National Airspace System by the Federal Aviation Authority, researchers can collect remote sensing data using UAS platforms. Researchers often customize the sensors on these UAS systems based on their specific requirements and use ad hoc processing steps to generate data.A challenge is that these data are often produced by a wide array of sensors and processes that render them potentially inconsistent with each other. Therefore, unlike data collected from metric sensors, UAS-based data are designed to benefit only specific user groups. The data thus generated often lack traceability to known standards, making them difficult to use with other geospatial data.This report provides radiometric and geometric calibration guidelines, as well as guidelines for data acquisition and quality control, that can be followed by USGS researchers in acquiring and processing UAS data. Instead of calibrating sensors, researchers collecting UAS data can focus on calibrating the data. Various radiometric calibration processes are provided, and the two panel empirical line method is highlighted for radiometric calibration.For geometric calibration, USGS and Department of the Interior researchers are experienced in following standard calibration procedures provided by standard UAS data processing software. However, researchers may be aided in paying attention to the tie points and the accuracy of the ground control points used for geometric calibration and data production. The accuracy of ground control points are related to the requirements of the project. The ground control points and the quality of tie points directly contribute to the geometric accuracy of the data, regardless of the ground sample distance of the imagery.The guidelines outlined in this report are intended to ensure that the data are in common units and are quantifiable and comparable with other data. These QA and calibration processes can be critical in ensuring that these datasets are used to the maximum extent possible. Including the calibration parameters (and their uncertainties) as part of metadata can allow for easier data discovery and analytical filters.", "description": "v, 23 p.", "publisher": { "@type": "Organization", "name": "U.S. Geological Survey" }, "author": [ { "@type": "Person", "name": "Sampath, Aparajithan", "givenName": "Aparajithan", "familyName": "Sampath", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-6922-4913", "url": "https://orcid.org/0000-0002-6922-4913" }, "affiliation": [ { "@type": "Organization", "name": "KBR, Inc., under contract to USGS" } ] }, { "@type": "Person", "name": "Shrestha, Mahesh", "givenName": "Mahesh", "familyName": "Shrestha", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-8368-6399", "url": "https://orcid.org/0000-0002-8368-6399" }, "affiliation": [ { "@type": "Organization", "name": "KBR, Inc., under contract to USGS" } ] }, { "@type": "Person", "name": "While, Michelle", "givenName": "Michelle", "familyName": "While", "affiliation": [ { "@type": "Organization", "name": "KBR, Inc., under contract to USGS" } ] }, { "@type": "Person", "name": "Scholl, Victoria Mary", "givenName": "Victoria Mary", "familyName": "Scholl", "identifier": { "@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0002-2085-1449", "url": "https://orcid.org/0000-0002-2085-1449" }, "affiliation": [ { "@type": "Organization", "name": "Geosciences and Environmental Change Science Center", "url": "https://www.usgs.gov/centers/geosciences-and-environmental-change-science-center" } ] } ], "funder": [ { "@type": "Organization", "name": "Earth Resources Observation and Science (EROS) Center", "url": "https://www.usgs.gov/centers/eros" } ] } }, "OpenAlex": { "abstract_inverted_index": { "First": [ 0 ], "posted": [ 1 ], "July": [ 2 ], "25,": [ 3 ], "2023": [ 4 ], "For": [ 5 ], "additional": [ 6 ], "information,": [ 7 ], "contact:": [ 8 ], "Director,": [ 9 ], "Earth": [ 10 ], 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