{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:02:37Z","timestamp":1760151757999,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,16]],"date-time":"2022-04-16T00:00:00Z","timestamp":1650067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (I2A), to the Lucas\u2013Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The I2A to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.<\/jats:p>","DOI":"10.3390\/jimaging8040116","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T04:21:28Z","timestamp":1650255688000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9914-2265","authenticated-orcid":false,"given":"Tran Xuan Bach","family":"Nguyen","sequence":"first","affiliation":[{"name":"School of Engineering, University of South Australia, Mawson Lakes 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kent","family":"Rosser","sequence":"additional","affiliation":[{"name":"Aerospace Division, Defence Science and Technology Group, Edinburgh 5111, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6496-0543","authenticated-orcid":false,"given":"Javaan","family":"Chahl","sequence":"additional","affiliation":[{"name":"School of Engineering, University of South Australia, Mawson Lakes 5095, Australia"},{"name":"Joint and Operations Analysis Division, Defence Science and Technology Group, Melbourne 3000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s00190-007-0165-x","article-title":"Fast error analysis of continuous GPS observations","volume":"82","author":"Bos","year":"2008","journal-title":"J. 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