Authors:
Toon Stuyck
1
;
Axel-Jan Rousseau
2
;
Mattia Vallerio
1
and
Eric Demeester
3
Affiliations:
1
BASF Antwerpen, BASF, Antwerpen, Belgium
;
2
Center for Statistics, Data Science Institute, UHasselt, Diepenbeek, Belgium
;
3
Department of Mechanical Engineering, ACRO Research Group, KU Leuven, Diepenbeek, Belgium
Keyword(s):
Generative Adversarial Networks, Cloud Detection, Structural Similarity, Image Segmentation, Anomaly Detection, Semi-supervised Learning.
Abstract:
Despite extensive efforts, it is still very challenging to correctly detect clouds automatically from RGB images. In this paper, an automated and effective cloud detection method is proposed based on a semi-supervised generative adversarial networks that was originally designed for anomaly detection in combination with structural similarity. By only training the networks on cloudless RGB images, the generator network is able to learn the distribution of normal input images and is able to generate realistic and contextually similar images. If an image with clouds is introduced, the network will fail to recreate a realistic and contextually similar image. Using this information combined with the structural similarity index, we are able to automatically and effectively segment anomalies, which in this case are clouds. The proposed method compares favourably to other commonly used cloud detection methods on RGB images.