发表机构
University of Windsor（温莎大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对全球野火频发，基于卫星图像的机器学习方法在野火检测有潜力。提出光谱形态注意力U-Net模型，含多种模块，用两数据集训练评估，模型在数据集中得分高，模块整合提高分割一致性，未来将跨数据集验证其通用性。
AI 中文摘要
在过去几十年里，全球野火发生频率持续上升。若能在早期检测并精确定位火灾，可最大程度降低其潜在危害。基于卫星图像的机器学习方法因能自动监测偏远广阔区域，在野火检测领域展现出巨大应用潜力。为此，我们提出了光谱形态注意力U-Net（SMA-UNet）模型，它包括光谱注意力模块、残差注意力U-Net主干、通道空间调制器和一对可微形态门。我们用两个数据集对该模型进行训练和评估，这些模块（除主干外）首次用于检测活跃火灾事件，特别是可微形态门是创新开发的。该模型在两个数据集中都取得了最高分（如在TS-SatFire中交并比为75.16%，在Sen2Fire中为22.50%）。通过对各模块进行消融研究，比较了它们的独立贡献并测试了组合情况。最终，这些模块的整合产生了一个高度鲁棒的框架，显著提高了在不同复杂环境条件下的分割一致性。未来工作将集中在跨不同卫星传感器的大规模多区域数据集验证该架构，以确立其在全球野火检测中的更广泛通用性。
英文摘要
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.
