  Very High Spatial Resolution (VHSR) large-scale SAR image databases are still
an unresolved issue in the Remote Sensing field. In this work, we propose such
a dataset and use it to explore patch-based classification in urban and
periurban areas, considering 7 distinct semantic classes. In this context, we
investigate the accuracy of large CNN classification models and pre-trained
networks for SAR imaging systems. Furthermore, we propose a Generative
Adversarial Network (GAN) for SAR image generation and test, whether the
synthetic data can actually improve classification accuracy.
