MyJournals Home  

RSS FeedsRemote Sensing, Vol. 13, Pages 4790: Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection (Remote Sensing)

 
 

26 november 2021 08:38:41

 
Remote Sensing, Vol. 13, Pages 4790: Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection (Remote Sensing)
 


This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.


 
61 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 4787: Geospatial Approaches to Monitoring the Spread of Invasive Species of Solidago spp. (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4789: Study on Spatiotemporal Evolution of the Yellow River Delta Coastline from 1976 to 2020 (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2021 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten