利用深度学习进行云像元填补以改进草地监测
Cloud gap-filling with deep learning for improved grassland monitoring
AI总结：
提出融合Sentinel-2光学与Sentinel-1 SAR数据的CNN-RNN深度学习云填补方法，生成连续NDVI序列并提升立陶宛草地刈割检测精度。
摘要：
不间断的光学图像时间序列对于及时监测农业用地变化至关重要，在草地中尤其如此。然而，此类时间序列的连续性经常被云层破坏。为应对这一挑战，我们提出一种创新的深度学习方法，融合无云光学（Sentinel-2）观测数据与不受天气影响的（Sentinel-1）合成孔径雷达（SAR）数据。该方法采用结合卷积神经网络（CNNs）和循环神经网络（RNNs）的混合架构，以生成连续的归一化植被指数（NDVI）时间序列，并凸显NDVI在SAR与光学数据协同中的作用。我们通过评估所生成的NDVI时间序列对草地刈割事件检测这一下游任务的影响，证明了观测连续性的重要性。研究在以大范围云覆盖为特征的立陶宛开展，并将所提方法与线性、Akima、二次等其他插值技术进行比较。我们的方法优于这些技术，取得了0.024的平均平均绝对误差（MAE）和0.92的决定系数R^2。此外，分析表明，采用两种广泛应用的刈割检测方法时，刈割事件检测性能有所提升，F1分数最高达84%。我们的方法还有效缓解了源自多云观测的突变和噪声；这些问题常被常规云掩膜遗漏，并对刈割检测精度产生不利影响。
英文摘要：
Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose an innovative deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data. Our approach employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate continuous Normalized Difference Vegetation Index (NDVI) time series, highlighting the role of NDVI in the synergy between SAR and optical data. We demonstrate the significance of observation continuity by assessing the impact of the generated NDVI time series on the downstream task of grassland mowing event detection. We conducted our study in Lithuania, a country characterized by extensive cloud coverage, and compared our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method outperformed these techniques, achieving an average Mean Absolute Error (MAE) of 0.024 and a coefficient of determination R^2 of 0.92. Additionally, our analysis revealed improvement in the performance of the mowing event detection, with F1-score up to 84% using two widely applied mowing detection methodologies. Our method also effectively mitigated sudden shifts and noise originating from cloudy observations, which are often missed by conventional cloud masks and adversely affect mowing detection precision.
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