期刊
REMOTE SENSING
卷 12, 期 24, 页码 -出版社
MDPI
DOI: 10.3390/rs12244169
关键词
forestry; fires; image processing; object segmentation
类别
资金
- Fundamental Technology Development Program for Extreme Disaster Response - Ministry of Interior and Safety, Korea [2019-MOIS31-011]
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2017R1A2B3007607]
Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.
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