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Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568
Article

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

1
Centre for Technological Risk Studies, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
2
IDLab, Ghent University – imec, 9502 Ghent, Belgium
3
Missoula Fire Sciences Lab, US Forest Service Rocky Mountain Research Station, Missoula, MT 59808, USA
4
National Center for Landscape Fire Analysis, University of Montana, Missoula, MT 59812, USA
*
Author to whom correspondence should be addressed.
Received: 17 December 2019 / Revised: 13 January 2020 / Accepted: 21 January 2020 / Published: 6 February 2020
(This article belongs to the Special Issue Remote Sensing and Image Processing for Fire Science and Management)
Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires. View Full-Text
Keywords: wildland fire; remote sensing; infrared imagery; video stabilization; image registration; sensitivity analysis; image similarity wildland fire; remote sensing; infrared imagery; video stabilization; image registration; sensitivity analysis; image similarity
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MDPI and ACS Style

Valero, M.M.; Verstockt, S.; Mata, C.; Jimenez, D.; Queen, L.; Rios, O.; Pastor, E.; Planas, E. Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis. Remote Sens. 2020, 12, 540. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030540

AMA Style

Valero MM, Verstockt S, Mata C, Jimenez D, Queen L, Rios O, Pastor E, Planas E. Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis. Remote Sensing. 2020; 12(3):540. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030540

Chicago/Turabian Style

Valero, Mario M., Steven Verstockt, Christian Mata, Dan Jimenez, Lloyd Queen, Oriol Rios, Elsa Pastor, and Eulàlia Planas. 2020. "Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis" Remote Sensing 12, no. 3: 540. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030540

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