부산대학교 도서관

Cloud segmentation of remotely sensed multispectral images is an important topic not only for weather forecast but, more in general, for establishing when the sensed data actually relate to the soil so that can be reliably used for some monitoring purpose. In this work, leveraging on the capability of convolutional neural networks to accurately approximate complex relationships between raw data and higher-level products, we propose a U-Net-like solution conceived for Sentinel-2 images. In order to face the scarsity of training data, a proper domain adaptation strategy has been pursued, which resorts to a labeled Landsat-8 dataset. Preliminary results show a consistent improvement over standard tools.