< Back to previous page
Semi-Supervised Cloud Detection with Weakly Labeled RGB Aerial Images using Generative Adversarial Networks
Book Contribution - Book Chapter Conference Contribution
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.
Book: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS (ICPRAM)
Pages: 630 - 635
Number of pages: 6
Keywords:Generative Adversarial Networks, Cloud Detection, Structural Similarity, Image Segmentation, Anomaly Detection, Semi-supervised Learning