基于尺度的卫星图像主动野火分割方法
Scale-based Approach for Active Wildfire Segmentation on Satellite Imagery
浏览论文内容
中文总结 AI 辅助
该研究针对卫星图像主动野火分割的稀疏不平衡问题，提出数据驱动协议评估模型鲁棒性，对比U-Net等架构与SWIR波段，发现U-Net鲁棒性最强、SWIR2效果最优，明确光谱选择与架构设计的重要性。
中文摘要 AI 辅助
从卫星图像进行主动野火制图颇具挑战性，因为火灾像素稀疏且高度不平衡，尤其是在早期或低密度火灾观测中。本研究探究了多光谱Landsat-8图像在多尺度野火规模条件下的主动火灾分割应用。我们提出了一种数据驱动协议，通过连通分量分析和四分位距准则表征火区规模分布，从而能评估模型在不同局部火区密度下的鲁棒性。对三种分割架构U-Net、DeepLabV3+和SegFormer在不同基于SWIR的光谱配置下进行评估。结果显示，U-Net在所有评估条件下鲁棒性最强，SegFormer表现具有竞争力，而DeepLabV3+倾向于产生保守预测且召回率降低。在所有架构中，SWIR2始终取得最强或接近最佳的结果，凸显其在Landsat-8图像主动火灾分割中的重要性。这些发现表明，对于基于低主动火灾像素密度图像训练的鲁棒卫星主动野火制图，光谱波段选择和架构设计均至关重要。
英文摘要
Active wildfire mapping from satellite imagery is challenging due to the sparse and highly imbalanced nature of fire pixels, especially in early-stage or low-density fire observations. This work investigates the use of multispectral Landsat-8 imagery for active-fire segmentation under multi-scale wildfire size conditions. We propose a data-driven protocol to characterize fire-region size distributions through connected-component analysis and an interquartile range criterion, enabling the evaluation of model robustness across different local fire-region densities. Three segmentation architectures, U-Net, DeepLabV3+, and SegFormer, are evaluated under different SWIR-based spectral configurations. Results show that U-Net achieves the strongest robustness across the evaluated conditions, SegFormer provides competitive performance, and DeepLabV3+ tends to produce conservative predictions with reduced recall. Across architectures, SWIR2 consistently achieves the strongest or near-best results, highlighting its importance for active-fire segmentation in Landsat-8 imagery. These findings suggest that both spectral band selection and architectural design are critical for robust satellite-based active wildfire mapping trained on low active fire-pixel density images.
