Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

by   Kenji Enomoto, et al.

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.


page 1

page 3

page 4

page 5

page 6

page 7

page 8


Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation

Satellite images are often contaminated by clouds. Cloud removal has rec...

MM811 Project Report: Cloud Detection and Removal in Satellite Images

For satellite images, the presence of clouds presents a problem as cloud...

Coloring panchromatic nighttime satellite images: Elastic maps vs. kernel smoothing and multivariate regression approach

Artificial light-at-night (ALAN), emitted from the ground and visible fr...

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

Satellite images hold great promise for continuous environmental monitor...

Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images using Deep Learning Techniques

This paper aims to detect rice field damage from natural disasters in Ba...

DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images

Optical satellite images are a critical data source; however, cloud cove...

Seeing Through Clouds in Satellite Images

This paper presents a neural-network-based solution to recover pixels oc...

Please sign up or login with your details

Forgot password? Click here to reset