• artificial intelligence
• fake satellite images
• gan
• generation
• generative adversarial network
• machine learning
• mantis
• satellite images
• sentinel-2
• synthetic dataset

Nowadays, the number of micro and nano satellites launched in the orbit for earth observation has increased rapidly. Thanks to the recent technological advancements, it is now possible to introduce artificial intelligence directly on board, while maintaining a very low power-consumption. In fact, the recent mission PhiSat-1 demonstrates for the first time the use of embedded low power Commercial Off-The-Shelf Myriad 2 Visual Processing Unit on-board of a satellite. It was successfully tested at CERN, where the Total Ionizing Dose was successfully evaluated, allowing to bring Deep Neural Networks directly on board.
This advancement opens new perspectives and possibilities, but it also introduces new challenges to overcome. An example of the latter is the MANTIS mission. It will use the same technology of the PhiSat-1 to reduce the bandwidth consumption featuring a Deep Neural Network. Unfortunately, the camera on board of the MANTIS satellite has no flight heritage i.e., it has never been used in an official mission. Thus, there is not a sufficient amount of data for training a dedicated Deep Neural Network.
This thesis focuses on the generation of synthetic satellite images for the MANTIS camera manufactured by Satlantis. For this purpose, the Generative Adversarial Networks (GANs) offer a promising solution to synthetic image generation. Recent advancements and studies conducted on novel GAN architectures show their capabilities in producing high-quality, high-resolution and high-diversity images. These capabilities are emphasized when working with relatively small and unbalanced datasets. Hence, the state-of-art GAN for image synthetization called StyleGAN2, was used to generate the synthetic dataset of MANTIS, starting from images taken by a similar satellite, called Sentinel-2. In particular, the best StyleGAN2 model was trained on 512x512 resolution, generating a dataset of about 10k images with the same properties of the MANTIS camera e.g., spatial resolution, colors, light, etc. The result of this synthesized dataset is very promising. In fact, it reached a Frechet Inception Distance of 20.732 and an Inception Score mean of 3.73 with a standard deviation of 0.07.