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In recent years, considerable advancements have been made in the area ofGenerative Adversarial Networks (GANs), particularly with the advent ofstyle-based architectures that address many key shortcomings - both in terms ofmodeling capabilities and network interpretability. Despite these improvements,the adoption of such approaches in the domain of satellite imagery is notstraightforward. Typical vision datasets used in generative tasks arewell-aligned and annotated, and exhibit limited variability. In contrast,satellite imagery exhibits great spatial and spectral variability, widepresence of fine, high-frequency details, while the tedious nature ofannotating satellite imagery leads to annotation scarcity - further motivatingdevelopments in unsupervised learning. In this light, we present the firstpre-trained style- and wavelet-based GAN model that can readily synthesize awide gamut of realistic satellite images in a variety of settings andconditions - while also preserving high-frequency information. Furthermore, weshow that by analyzing the intermediate activations of our network, one candiscover a multitude of interpretable semantic directions that facilitate theguided synthesis of satellite images in terms of high-level concepts (e.g.,urbanization) without using any form of supervision. Via a set of qualitativeand quantitative experiments we demonstrate the efficacy of our framework, interms of suitability for downstream tasks (e.g., data augmentation), quality ofsynthetic imagery, as well as generalization capabilities to unseen datasets.

Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models