Identifying Forest Burned Area Using a Deep Learning Model Based on Post-Fire Optical and SAR Remote Sensing Images
Identifying wildfire burned areas using satellite images is significant for effectively monitoring the status of forests. The full utilization of multi-source satellite images that provide complementary information is beneficial for accurate monitoring of Forest-Burned Area (FBA), which, however, is...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10792922/ |
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| Summary: | Identifying wildfire burned areas using satellite images is significant for effectively monitoring the status of forests. The full utilization of multi-source satellite images that provide complementary information is beneficial for accurate monitoring of Forest-Burned Area (FBA), which, however, is ignored by many current studies. In this paper, inspired by the Residual-based U-Net (RU-Net), an innovative deep learning-based model, DARU-Net, for FBA Identification (FBAI) using multi-source satellite images is presented based on a dual-path mechanism and an attention module. The proposed DARU-Net employs a dual-path mechanism to mine complementary information from Sentinel-1 Synthetic Aperture Radar (SAR) image and Sentinel-2 optical image. Besides, a channel-spatial attention residual (CSAR) module is embedded into the network, aiming at helping the network to focus on useful information. The experimental results on benchmark FBAI datasets demonstrate the good performance of DARU-Net in identifying wildfire burned areas, with an overall accuracy of 93.14% and a F-score of 83.01%, outperformed some widely-used U-Net-based detection models. The DARU-Net is more capable of accurately identifying FBAs by preserving geometrical details due to the use of dual-path integration module. Besides, it is found that, the CSAR module is helpful to promote both the precision and the training efficiency of the proposed model. |
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| ISSN: | 2169-3536 |