{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:39:59Z","timestamp":1766137199452,"version":"3.37.3"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,4,14]],"date-time":"2019-04-14T00:00:00Z","timestamp":1555200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF) grant funded by the Korea Government","award":["2017R1C1B2005744"],"award-info":[{"award-number":["2017R1C1B2005744"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2019,4,14]]},"abstract":"<jats:p>Image coregistration is a key preprocessing step to ensure the effective application of very-high-resolution (VHR) orthophotos generated from multisensor images acquired from unmanned aerial vehicle (UAV) platforms. The most accurate method to align an orthophoto is the installation of air-photo targets at a test site prior to flight image acquisition, and these targets were used as ground control points (GCPs) for georeferencing and georectification. However, there are time and cost limitations related to installing the targets and conducting field surveys on the targets during every flight. To address this problem, this paper presents an automated coregistration approach for orthophotos generated from VHR images acquired from multisensors mounted on UAV platforms. Spatial information from the orthophotos, provided by the global navigation satellite system (GNSS) at each image\u2019s acquisition time, is used as ancillary information for phase correlation-based coregistration. A transformation function between the multisensor orthophotos is then estimated based on conjugate points (CPs), which are locally extracted over orthophotos using the phase correlation approach. Two multisensor datasets are constructed to evaluate the proposed approach. These visual and quantitative evaluations confirm the superiority of the proposed method.<\/jats:p>","DOI":"10.1155\/2019\/2962734","type":"journal-article","created":{"date-parts":[[2019,4,14]],"date-time":"2019-04-14T19:30:51Z","timestamp":1555270251000},"page":"1-10","source":"Crossref","is-referenced-by-count":12,"title":["Automated Coregistration of Multisensor Orthophotos Generated from Unmanned Aerial Vehicle Platforms"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6586-8503","authenticated-orcid":true,"given":"Youkyung","family":"Han","sequence":"first","affiliation":[{"name":"School of Convergence & Fusion System Engineering, Kyungpook National University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-6481","authenticated-orcid":true,"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Chungbuk National University, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinha","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjin","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungchan","family":"Oh","sequence":"additional","affiliation":[{"name":"School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7571-1155","authenticated-orcid":true,"given":"Junho","family":"Yeom","sequence":"additional","affiliation":[{"name":"Research Institute for Automotive Diagnosis Technology of Multi-Scale Organic and 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