Obstruction detection and recovery in weather radar images using deep learning

Obstruction is one of the significant sources of errors impacting the quality of weather radar observations. During the radar's operation, it encounters obstructions from terrains and tall buildings. These obstructions interfere with the radar signal's propagation and reception, leading to incomplete or inaccurate meteorological data in certain areas. In this paper, we introduce a novel approach, termed DOR, which initially detects obstruction masks and then recovers these regions using deep learning. Specifically, we incorporate DenseASPP into the final layer of the Unet encoder network, enriching the feature maps post-convolution with comprehensive spatial-semantic information. This design ensures that the encoder extracts highly-correlated contour detail features from the lower layers, achieving precise segmentation and localization of the target. Subsequently, we utilize generative adversarial networks (GAN) to recover the obscured areas. Extensive experiments on real weather radar images demonstrate that our proposed DOR method can effectively detect and recover.