用于卫星云掩码降尺度的深度学习超分辨率方法
Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
- BEYOND EO Centre（BEYOND EO中心）
- IAASARS, National Observatory of Athens（雅典国家天文台IAASARS研究所）
- National and Kapodistrian University of Athens（雅典国立及卡波迪斯特里亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出两种深度学习超分辨率方法及跨传感器云掩码数据集SEVMOD-CM，实现SEVIRI云掩码4倍空间增强，可用于大气监测等遥感相关应用。
AI中文摘要：
每天有海量光学卫星数据被传输至地面服务器，其中超过一半的数据受雾霾或云层影响，此外，这些数据还存在空间分辨率与时间分辨率之间的固有权衡问题，该问题至今未得到有效解决，使得获取连续的高分辨率卫星云观测数据仍是一项持续存在的挑战。本研究针对该挑战，提出两种深度学习超分辨率方法，用于对SEVIRI云掩码产品进行精确降尺度，同时创建了名为SEVMOD-CM的新型跨传感器云掩码数据集，该数据集通过对MODIS与SEVIRI卫星观测数据进行时空匹配构建而成。所提出的两种模型分别为基于卷积神经网络（CNN）的SpatialCNN和基于生成对抗网络（GAN）的SpatialGAN，经SEVIRI光谱数据及云掩码产品训练后，可预测对应的MODIS云掩码，实现跨传感器域4倍空间增强。对两种方法进行实验评估，并与标准双三次插值上采样技术对比，实验结果证明了所提模型与数据集对遥感领域的价值，凸显了将超分辨率技术应用于地球静止卫星衍生的云掩码产品的益处，可用于大气监测、天气预报、灾害风险降低、太阳能预报及气候研究等应用场景。
英文摘要：
A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or clouds. Additionally, this data suffers from the fundamental trade-off between spatial and temporal resolution, which remains largely unresolved, making the acquisition of continuous high-resolution satellite observations of clouds an ongoing challenge. This work addresses this challenge by proposing two Deep Learning super-resolution methods for the accurate downscaling of SEVIRI cloud mask products, as well as a novel cross-sensor cloud mask dataset called SEVMOD-CM, created by spatially and temporally matching MODIS and SEVIRI satellite observations. The two proposed models are a CNN-based (SpatialCNN) and a GAN-based (SpatialGAN) Neural Network. Trained on the SEVIRI spectral and cloud mask products, the proposed methods predict the corresponding MODIS Cloud masks, achieving a 4x spatial enhancement across sensor domains. Both approaches are evaluated experimentally, and compared against the standard bicubic interpolation upsampling technique. The experimental results demonstrate the value of the proposed models and dataset for the remote sensing community, highlighting the benefits of applying super-resolution techniques to geostationary-derived cloud mask products for applications such as atmospheric monitoring, weather forecasting, disaster risk reduction, solar energy forecasting, and climate research.
