Spectral-Morphological Attention U-Net: An Efficient Network for Active Wildfire Detection

• URL: http://arxiv.org/abs/2607.16472v1
• Date: Fri, 17 Jul 2026 19:37:16 GMT
• Title: Spectral-Morphological Attention U-Net: An Efficient Network for Active Wildfire Detection
• Abstract summary: We propose a new model named spectral-morphological attention U-Net(SMA-UNet), which includes a spectral attention module, a residual attention UNet backbone, a channel-spatial modulator, and a pair of differentiable morphological gates.<n>These modules, excluding the backbone, are used to detect active fire events for the first time, especially the pair of differentiable morphological gates.
• Score: 0.692305735698369
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily. Therefore, if the fire can be detected and precisely located at an early stage, the potential hazards caused by it can be minimized to the greatest extent. The machine learning methods based on satellite images, due to their ability to automatically monitor extremely remote and vast areas, have shown great potential for application in the field of wildfire detection. To address this challenge, we proposed a new model named spectral-morphological attention U-Net(SMA-UNet), which includes a spectral attention module, a residual attention UNet backbone, a channel-spatial modulator, and a pair of differentiable morphological gates. We trained and evaluated this model with two datasets. These modules, excluding the backbone, are used to detect active fire events for the first time, especially the pair of differentiable morphological gates, which is innovatively developed. The proposed model achieved the highest scores in both datasets (e.g., intersection over union 75.16% in TS-SatFire, 22.50% in Sen2Fire). By conducting ablation studies of each module, we compared their independent contributions and tested their combinations. Ultimately, the integration of these modules yields a highly robust framework that significantly improves segmentation consistency across diverse and complex environmental conditions. Future work will focus on validating the proposed architecture across large-scale, multi-regional datasets from different satellite sensors to establish its broader generalizability for global wildfire detection.

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