Improved Active Fire Detection Using Operational U-nets

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As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial in environmental monitoring. Using satellite imagery to monitor and detect active fires has been critical for managing forests and public land. Many traditional statistical-based methods and more recent deep-learning techniques have been proposed for active fire detection. In this study, we propose a novel approach called Operational U-Nets for the improved early detection of active fires. The proposed approach utilizes Self-Organized Operational Neural Network (Self-ONN) layers in a compact U-Net architecture. The preliminary experimental results demonstrate that Operational U-Nets not only achieve superior detection performance but can also significantly reduce computational complexity.
Original languageEnglish
Title of host publicationProgress in Electromagnetic Research Symposium
ISBN (Electronic)979-8-3503-1284-3
Publication statusPublished - 28 Aug 2023
Publication typeA4 Article in conference proceedings
EventPhotonics & Electromagnetics Research Symposium - Prague, Czech Republic
Duration: 3 Jul 20236 Jul 2023

Publication series

ISSN (Print)2831-5790
ISSN (Electronic)2831-5804


ConferencePhotonics & Electromagnetics Research Symposium
Country/TerritoryCzech Republic

Publication forum classification

  • Publication forum level 1


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