AF-Net: An Active Fire Detection Model Using Improved Object-Contextual Representations on Unbalanced UAV Datasets
Xikun Hu,
Wenlin Liu,
Hao Wen
et al.
Abstract:Active fire detection is essential for early warning of wildfires to help suppress and mitigate damage. This study presents an AF-Net model based on object-contextual representations (OCR) for active fire segmentation from very high-resolution (VHR) unmanned aerial vehicles (UAVs) remote sensing images. To efficiently detect heat anomalies in forests from large UAV scenes, we have to handle the class imbalance between small active fire pixels and large-area complex background information. Class imbalance affec… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.