WISE：一种用于星上火灾烟雾检测与定位的轻量级弱监督模型
WISE: A Lightweight, Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization
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中文总结 AI 辅助
针对星上野火烟雾检测的延迟与内存限制，提出轻量级弱监督框架WISE，利用师生蒸馏仅用瓦片级标注生成烟雾概率图，在轨实测每瓦片0.10-0.26秒，最佳F1达0.964/0.750，参数仅0.12M。
中文摘要 AI 辅助
从卫星图像中检测野火烟雾对于早期预警和快速响应至关重要。对于星上卫星部署，检测系统必须在严格的内存和延迟限制下运行，同时为下游决策提供具有空间信息性的输出。现有的瓦片级分类方法计算效率高但缺乏空间定位能力，而像素级分割方法能提供详细的掩膜，但通常计算量过大，难以在实时星上执行。为解决这一差距，我们提出了WISE（弱监督高效推理烟雾提取），一种面向部署的星上火灾烟雾检测与定位框架。WISE仅利用瓦片级标注，通过师生蒸馏策略，其中离线教师为轻量级WISE-Student提供软空间监督，后者针对高效星上推理进行了优化。学生模型在单次前向传播中联合预测瓦片级烟雾存在性和烟雾概率图，从而在严格计算约束下实现具有空间信息性的检测。WISE通过在ISS搭载的IMAGIN-e载荷上的在轨执行进行了评估。三种模型变体实现了每瓦片平均推理时间分别为0.10秒、0.14秒和0.26秒，表明在星上资源限制内实现了近实时的每瓦片推理。基于Landsat 5和Landsat 8影像的地面实验进一步表明其有效的检测和具有空间信息性的定位。最佳变体在10次运行中实现了平均瓦片级F1分数0.964和平均像素级F1分数0.750，同时仅包含0.12M参数，且需要约3 GFLOPs。综合这些结果表明，WISE是在星上资源约束下进行低延迟野火烟雾监测的实用候选方案。
英文摘要
Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems must operate under strict memory and latency constraints while providing spatially informative outputs for downstream decision-making. Existing tile-level classification methods are computationally efficient but lack spatial localization, whereas pixel-level segmentation approaches provide detailed masks yet are typically too computationally demanding for real-time onboard execution. To address this gap, we propose WISE (Weakly-supervised Inference-efficient Smoke Extraction), a deployment-oriented framework for onboard fire smoke detection and localization. WISE leverages only tile-level annotations through a teacher-student distillation strategy, where an offline teacher provides soft spatial supervision to a lightweight WISE-Student optimized for efficient onboard inference. The student jointly predicts tile-level smoke presence and smoke probability maps within a single forward pass, enabling spatially informative detection under strict computational constraints. WISE was evaluated through in-orbit execution aboard the ISS-mounted IMAGIN-e payload. Three model variants achieve average inference times of 0.10 s, 0.14 s, and 0.26 s per tile, indicating near-real-time per-tile inference within onboard resource limits. Ground-based experiments on Landsat 5 and Landsat 8 imagery further indicate effective detection and spatially informative localization. The best-performing variant achieves a mean tile-level F1 score of 0.964 and a mean pixel-level F1 score of 0.750 across 10 runs, while containing only 0.12M parameters and requiring approximately 3 GFLOPs. Together, these results indicate that WISE is a practical candidate for low-latency wildfire smoke monitoring from space under onboard resource constraints.
发表机构
- Adelaide University（阿德莱德大学）
- Swinburne University of Technology（斯威本科技大学）
- Thales Alenia Space（泰雷兹阿莱尼亚宇航公司）
机构由 AI 辅助整理，请以论文原文为准。
