Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks

2024-01-22

arXiv.org

AI Summary

Nighttime tropical cyclone monitoring suffers from discontinuous, low-resolution observations due to the absence of visible-light imagery. To address this, this paper proposes a cross-modal image synthesis method based on Conditional Generative Adversarial Networks (CGANs). The approach innovatively employs multi-channel infrared remote sensing data—including day–satellite geometric parameters—as conditional inputs, replaces the conventional L1 loss with Structural Similarity Index Measure (SSIM) loss to mitigate output blurriness, and synergistically fuses Himawari-8 Advanced Himawari Imager (AHI) and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data to reconstruct nighttime visible-light imagery guided by daytime observations. Experiments demonstrate strong performance: on the AHI daytime validation set, SSIM reaches 0.923 and RMSE is 0.0299; for cross-satellite nighttime VIIRS DNB image generation, the method significantly outperforms existing approaches and has been deployed operationally.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Visible (VIS) imagery is important for monitoring Tropical Cyclones (TCs) but is unavailable at night. This study presents a Conditional Generative Adversarial Networks (CGAN) model to generate nighttime VIS imagery with significantly enhanced accuracy and spatial resolution. Our method offers three key improvements compared to existing models. First, we replaced the L1 loss in the pix2pix framework with the Structural Similarity Index Measure (SSIM) loss, which significantly reduced image blurriness. Second, we selected multispectral infrared (IR) bands as input based on a thorough examination of their spectral properties, providing essential physical information for accurate simulation. Third, we incorporated the direction parameters of the sun and the satellite, which addressed the dependence of VIS images on sunlight directions and enabled a much larger training set from continuous daytime data. The model was trained and validated using data from the Advanced Himawari Imager (AHI) in the daytime, achieving statistical results of SSIM = 0.923 and Root Mean Square Error (RMSE) = 0.0299, which significantly surpasses existing models. We also performed a cross-satellite nighttime model validation using the Day/Night Band (DNB) of the Visible/Infrared Imager Radiometer Suite (VIIRS), which yields outstanding results compared to existing models. Our model is operationally applied to generate accurate VIS imagery with arbitrary virtual sunlight directions, significantly contributing to the nighttime monitoring of various meteorological phenomena.

Problem

• Generates nighttime visible satellite imagery for tropical cyclones.
• Improves accuracy using SSIM loss and multispectral IR bands.
• Incorporates sunlight and satellite direction for better training.

Innovation

• CGAN model for nighttime VIS imagery simulation
• SSIM loss reduces image blurriness effectively
• Multispectral IR bands enhance simulation accuracy