{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:47:43Z","timestamp":1775101663875,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T00:00:00Z","timestamp":1559779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NUIST-Wuxi Research Institute","award":["This is a general funding and doesn't have a specific grant number"],"award-info":[{"award-number":["This is a general funding and doesn't have a specific grant number"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) support a large array of technological applications and scientific studies due to their ability to collect high-resolution image data. The processing of UAV data requires the use of mosaicking technology, such as structure-from-motion, which combines multiple photos to form a single image mosaic and to construct a 3-D digital model of the measurement target. However, the mosaicking of thermal images is challenging due to low lens resolution and weak contrast in the single thermal band. In this study, a novel method, referred to as four-band thermal mosaicking (FTM), was developed in order to process thermal images. The method stacks the thermal band obtained by a thermal camera onto the RGB bands acquired on the same flight by an RGB camera and mosaics the four bands simultaneously. An object-based calibration method is then used to eliminate inter-band positional errors. A UAV flight over a natural park was carried out in order to test the method. The results demonstrated that with the assistance of the high-resolution RGB bands, the method enabled successful and efficient thermal mosaicking. Transect analysis revealed an inter-band accuracy of 0.39 m or 0.68 times the ground pixel size of the thermal camera. A cluster analysis validated that the thermal mosaic captured the expected contrast of thermal properties between different surfaces within the scene.<\/jats:p>","DOI":"10.3390\/rs11111365","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T03:56:31Z","timestamp":1559879791000},"page":"1365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Four-band Thermal Mosaicking: A New Method to Process Infrared Thermal Imagery of Urban Landscapes from UAV Flights"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-0295","authenticated-orcid":false,"given":"Yichen","family":"Yang","sequence":"first","affiliation":[{"name":"Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China"},{"name":"Yale School of Forestry &amp; Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA"}]},{"given":"Xuhui","family":"Lee","sequence":"additional","affiliation":[{"name":"Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China"},{"name":"Yale School of Forestry &amp; Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/10106049.2010.539302","article-title":"The Ikhana unmanned airborne system (UAS) western states fire imaging missions: From concept to reality (2006\u20132010)","volume":"26","author":"Ambrosia","year":"2011","journal-title":"Geocart. 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