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Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU
The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123897/ https://www.ncbi.nlm.nih.gov/pubmed/33925366 http://dx.doi.org/10.3390/s21093047 |
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author | Ifimov, Gabriela Naprstek, Tomas Johnston, Joshua M. Arroyo-Mora, Juan Pablo Leblanc, George Lee, Madeline D. |
author_facet | Ifimov, Gabriela Naprstek, Tomas Johnston, Joshua M. Arroyo-Mora, Juan Pablo Leblanc, George Lee, Madeline D. |
author_sort | Ifimov, Gabriela |
collection | PubMed |
description | The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires. |
format | Online Article Text |
id | pubmed-8123897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81238972021-05-16 Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU Ifimov, Gabriela Naprstek, Tomas Johnston, Joshua M. Arroyo-Mora, Juan Pablo Leblanc, George Lee, Madeline D. Sensors (Basel) Article The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires. MDPI 2021-04-27 /pmc/articles/PMC8123897/ /pubmed/33925366 http://dx.doi.org/10.3390/s21093047 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ifimov, Gabriela Naprstek, Tomas Johnston, Joshua M. Arroyo-Mora, Juan Pablo Leblanc, George Lee, Madeline D. Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title | Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title_full | Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title_fullStr | Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title_full_unstemmed | Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title_short | Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU |
title_sort | geocorrection of airborne mid-wave infrared imagery for mapping wildfires without gps or imu |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123897/ https://www.ncbi.nlm.nih.gov/pubmed/33925366 http://dx.doi.org/10.3390/s21093047 |
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