ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 279–285, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-279-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 279–285, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-279-2020

  03 Aug 2020

03 Aug 2020

GENERATING ARTIFICIAL NEAR INFRARED SPECTRAL BAND FROM RGB IMAGE USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK

X. Yuan, J. Tian, and P. Reinartz X. Yuan et al.
  • Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Germany

Keywords: Near-infrared, RGB, Gerative adversarial networks, Robust loss function, Conditional GAN

Abstract. Near infrared bands (NIR) provide rich information for many remote sensing applications. In addition to deriving useful indices to delineate water and vegetation, near infrared channels could also be used to facilitate image pre-processing. However, synthesizing bands from RGB spectrum is not an easy task. The inter-correlations between bands are not clearly identified in physical models. Generative adversarial networks (GAN) have been used in many tasks such as generating photorealistic images, monocular depth estimation and Digital Surface Model (DSM) refinement etc. Conditional GAN is different in that it observes some data as a condition. In this paper, we explore a cGAN network structure to generate a NIR spectral band that is conditioned on the input RGB image. We test different discriminators and loss functions, and evaluate results using various metrics. The best simulated NIR channel has a mean absolute error of around 5 percent in Sentinel-2 dataset. In addition, the simulated NIR image can correctly distinguish between various classes of landcover.