Creating synthetic night-time visible-light meteorological satellite images using the GAN method
Year: 2022
Type: article
Abstract: Meteorology satellite visible-light images are critical for meteorologists. However, there are no satellite visible-light channels data at night, so we propose a method based on deep learning to create synthetic satellite visible-light images during night. Specifically, to produce realistic-looking products, we trained a generative adversarial network (GAN) model. The model can generate satellite visible-light images from corresponding satellite infrared (IR) channels data and numerical weather prediction (NWP) products. Considering to explicitly evaluating the contributions of different satellite IR channels and NWP products elements, we suggest using a channel-wise attention mechanic, e.g. a ‘Squeeze and Extraction Block’ (SEBlock) to quantitatively weigh the importance of different input data channels. The experiments based on the NWP products and the meteorology satellite data show that the proposed method is effective to create realistic synthetic satellite visible-light images during night.
Source: Remote Sensing Letters
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G5681433360
OpenAlex ID: W4281772014