Problem
Research questions and friction points this paper is trying to address.
Deep Learning
Super Resolution
Satellite Cloud Mask
Spatial and Temporal Resolution
Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
🤖 AI Summary
本文针对卫星云图数据分辨率低的问题，提出两种基于深度学习的超分辨率方法（SpatialCNN和SpatialGAN）来提高SEVIRI云掩模产品的空间分辨率。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Learning Super Resolution
Cloud Mask Downscaling
Cross-sensor Dataset
SpatialCNN
SpatialGAN
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