A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity Mapping Article Swipe
Mingda Wu
,
Qunying Huang
,
Tang Sui
,
Bo Peng
,
Manzhu Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3460531
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3460531
Wildfires cause substantial damage and present considerable risks to both natural ecosystem and human societies. A precise and prompt evaluation of wildfire-induced damage is crucial for effective postfire management and restoration. Considerable advancements have been made in monitoring and mapping fire-affected areas through feature engineering and machine learning techniques. However, existing methods often exhibit several limitations, such as complicated and time-intensive procedures on manual labeling, and a primary focus on binary classification, which only distinguishes between burned and nonburned areas. In response, this study develops a wildfire burn severity assessment model, BiRAUnet-NBR, which can not only accurately identify fire-affected areas, but also assess the burn severity levels (low, moderate, and high) within those areas. Built upon the standard U-Net architecture, the proposed BiRAUnet-NBR first incorporates bitemporal Sentinel 2 Level-2A remote sensing imagery, captured before and after a wildfire, which enables the model to better distinguish burned areas from the background and identify the severity level of the resulting burns. In addition, it further enhances the standard U-Net architecture by fusing additional spectral layers, such as the normalized burn ratio (NBR) derived from post- and prefire images, therefore, informing the detection of burn areas. Moreover, BiRAUnet-NBR also integrates attention mechanism, enabling the model to pay more attention to meaningful features and burn areas, and residual blocks in the decoder module, which not only significantly improves segmentation results but also enhances training stability and prevents the issue of vanishing gradients. The experimental results demonstrate the superiority of the proposed model in both multiclass and binary mapping of wildfire burn areas, achieving an overall accuracy over 95%. Furthermore, it outperforms baseline algorithms, including support vector machine, random forest, eXtreme gradient boosting, and fully convolutional network, with an average improvement of 18% in F1-score and 15% in mean intersection over union.
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All OpenAlex metadata
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https://openalex.org/W4402509506Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/jstars.2024.3460531Digital Object Identifier
- Title
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A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity MappingWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Mingda Wu, Qunying Huang, Tang Sui, Bo Peng, Manzhu YuList of authors in order
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https://doi.org/10.1109/jstars.2024.3460531Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2024.3460531Direct OA link when available
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Remote sensing, Residual, Environmental science, Index (typography), Computer science, Meteorology, Geology, Geography, Algorithm, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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