论文标题
通过粗略监督的光滑U-NET监视极端分辨率的空间的植被
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
论文作者
论文摘要
在极端分辨率下监测植被的生产力对于现实世界中的农业应用非常有价值，例如检测作物压力和提供粮食不安全的预警。太阳能诱导的叶绿素荧光（SIF）提供了一种直接从空间中测量植物生产力的有希望的方法。但是，卫星SIF观察只能以粗空间分辨率进行，因此无法监视单个农作物类型或农场的工作方式。这构成了一项具有挑战性的粗略监督回归（或缩小）任务；在训练时，我们只有粗分辨率（3公里）的SIF标签，但我们希望以更精细的空间分辨率预测SIF（例如30m，增加了100倍）。我们还具有其他精细分辨率输入特征，但是这些功能与SIF之间的关系尚不清楚。为了解决这个问题，我们提出了一种粗糙的平滑U-NET（CS-Sunet），这是这种粗糙监督设置的新方法。 CS-Sunet基于先验知识（例如平滑度损失），将深卷积网络的表达能力与新颖的正则化方法相结合，这对于防止过度拟合至关重要。实验表明，与现有方法相比，CS-Sunet更准确地解决SIF中的细粒变化。
Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods.
