  In this paper, we propose a method for cloud removal from visible light RGB
satellite images by extending the conditional Generative Adversarial Networks
(cGANs) from RGB images to multispectral images. Satellite images have been
widely utilized for various purposes, such as natural environment monitoring
(pollution, forest or rivers), transportation improvement and prompt emergency
response to disasters. However, the obscurity caused by clouds makes it
unstable to monitor the situation on the ground with the visible light camera.
Images captured by a longer wavelength are introduced to reduce the effects of
clouds. Synthetic Aperture Radar (SAR) is such an example that improves
visibility even the clouds exist. On the other hand, the spatial resolution
decreases as the wavelength increases. Furthermore, the images captured by long
wavelengths differs considerably from those captured by visible light in terms
of their appearance. Therefore, we propose a network that can remove clouds and
generate visible light images from the multispectral images taken as inputs.
This is achieved by extending the input channels of cGANs to be compatible with
multispectral images. The networks are trained to output images that are close
to the ground truth using the images synthesized with clouds over the ground
truth as inputs. In the available dataset, the proportion of images of the
forest or the sea is very high, which will introduce bias in the training
dataset if uniformly sampled from the original dataset. Thus, we utilize the
t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of
bias in the training dataset. Finally, we confirm the feasibility of the
proposed network on the dataset of four bands images, which include three
visible light bands and one near-infrared (NIR) band.
