Thermal Infrared Video Stabilization for Aerial Monitoring of Active Wildfires

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

LLoyd Queen¹  Mario Miguel Valero¹  Eulalia Planas¹  Bret Butler²  Oriol Rios³  Christian Mata³  Elsa Pastor⁴  Steven Verstockt⁴  Daniel Jimenez⁵ 

[1] Centre for Technological Risk Studies, Universitat Polit&x00E8;IDLab, Ghent University&x2014;Missoula Fire Sciences Lab, US Forest Service Rocky Mountain Research Station, Missoula, MT, USA;cnica de Catalunya, Barcelona, Spain;imec, Ghent, Belgium;

关键词: Fire behavior;    image registration;    KAZE;    remote sensing;    unmanned aerial systems (UAS);    video stabilization;   

DOI  :  10.1109/JSTARS.2021.3059054

来源: DOAJ

【 摘 要 】

Measuring wildland fire behavior is essential for fire science and fire management. Aerial thermal infrared (TIR) imaging provides outstanding opportunities to acquire such information remotely. Variables such as fire rate of spread (ROS), fire radiative power (FRP), and fireline intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behavior to weather, vegetation, topography, and firefighting efforts. However, raw TIR imagery acquired by unmanned aerial vehicles (UAVs) requires stabilization and georeferencing before any other processing can be performed. Aerial video usually suffers from instabilities produced by sensor movement. This problem is especially acute near an active wildfire due to fire-generated turbulence. Furthermore, the nature of fire TIR video presents some specific challenges that hinder robust interframe registration. Therefore, this article presents a software-based video stabilization algorithm specifically designed for TIR imagery of forest fires. After a comparative analysis of existing image registration algorithms, the KAZE feature-matching method was selected and accompanied by pre- and postprocessing modules. These included foreground histogram equalization and a multireference framework designed to increase the algorithm's robustness in the presence of missing or faulty frames. The performance of the proposed algorithm was validated in a total of nine video sequences acquired during field fire experiments. The proposed algorithm yielded a registration accuracy between 10 and 1000× higher than other tested methods, returned 10× more meaningful feature matches, and proved robust in the presence of faulty video frames. The ability to automatically cancel camera movement for every frame in a video sequence solves a key limitation in data processing pipelines and opens the door to a number of systematic fire behavior experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency.

【 授权许可】

Unknown