Multi-class semantic segmentation of wildfire fuel models in Sentinel-2 imagery using R2U-Net

Rayan Afsar; Vijayan K. Asari

doi:10.1117/12.2663647

13 June 2023 Multi-class semantic segmentation of wildfire fuel models in Sentinel-2 imagery using R2U-Net

Rayan Afsar, Vijayan K. Asari

Proceedings Volume 12527, Pattern Recognition and Tracking XXXIV; 125270I (2023) https://doi.org/10.1117/12.2663647
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Wildfires have greatly increased in frequency and intensity over the past decade in the American West. The need for up-to-date data on the fuels that make up a fire regime is crucial. Semantic segmentation algorithms such as U-Net have proven very effective at similar tasks in biomedical imaging and geospatial imagery due to the powerful feature extraction from these deep networks. In this paper, we compare the U-Net and Recurrent Residual U-Net (R2U-Net) architectures’ performance on multi-class pixel-wise segmentation. A portion of Northern California was the target area for the study, and segmentation masks were based on the 40 Scott & Burgan fuel models, utilizing Sentinel-2 imagery and LANDFIRE’s LF2020 survey data. After training, the R2U-Net achieved an accuracy of 59.64% while the U-Net achieved an accuracy of 61.28%.

Conference Presentation

Citation Download Citation

Rayan Afsar and Vijayan K. Asari "Multi-class semantic segmentation of wildfire fuel models in Sentinel-2 imagery using R2U-Net", Proc. SPIE 12527, Pattern Recognition and Tracking XXXIV, 125270I (13 June 2023); https://doi.org/10.1117/12.2663647

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