Nighttime Reflectance Generation in the Visible Band of Satellites

Remote Sensing

Yerin Kim¹  Sungwook Hong¹  Ji-Hye Kim¹  Yong-Jae Moon²  Taeyoung Kim²  Gyungin Shin²  Eunsu Park²  Kimoon Kim² 

[1] Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul 05006, Korea;School of Space Research, Kyung Hee University, Gyeonggi-do 17104, Korea;

关键词: deep learning;    CGAN;    visible;    infrared;    reflectance;    radiance;    satellite remote sensing;   

DOI  :  10.3390/rs11182087

来源: DOAJ

【 摘 要 】

Visible (VIS) bands, such as the 0.675 μm band in geostationary satellite remote sensing, have played an important role in monitoring and analyzing weather and climate change during the past few decades with coarse spatial and high temporal resolution. Recently, many deep learning techniques have been developed and applied in a variety of applications and research fields. In this study, we developed a deep-learning-based model to generate non-existent nighttime VIS satellite images using the Conditional Generative Adversarial Nets (CGAN) technique. For our CGAN-based model training and validation, we used the daytime image data sets of reflectance in the Communication, Ocean and Meteorological Satellite / Meteorological Imager (COMS/MI) VIS (0.675 μm) band and radiance in the longwave infrared (10.8 μm) band of the COMS/MI sensor over five years (2012 to 2017). Our results show high accuracy (bias = −2.41 and root mean square error (RMSE) = 36.85 during summer, bias = −0.21 and RMSE = 33.02 during winter) and correlation (correlation coefficient (CC) = 0.88 during summer, CC = 0.89 during winter) of values between the observed images and the CGAN-generated images for the COMS VIS band. Consequently, our CGAN-based model can be effectively used in a variety of meteorological applications, such as cloud, fog, and typhoon analyses during daytime and nighttime.

【 授权许可】

Unknown