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InfoResearchPeer-reviewed
Integrating GAN and Dynamic Identity Convolution for Enhanced Radar Image Reconstruction From Geostationary Satellite Observations
- Published
- Record updated
Summary
Researchers propose DIC-GAN, a generative adversarial network that reconstructs radar reflectivity mosaic images from multichannel geostationary satellite observations. The generator uses nine dynamic identity convolution modules and a mixed loss combining BCE, MSE and weighted threshold terms to better capture strong reflectivity regions. The authors report that DIC-GAN outperforms comparison methods and generalizes to areas without radar coverage.