Locality-preserving Directions for Interpreting the Latent Space of
  Satellite Image GANs
Georgia Kourmouli Nikos Kostagiolas Yannis Panagakis Mihalis A. Nicolaou
We present a locality-aware method for interpreting the latent space of
wavelet-based Generative Adversarial Networks (GANs), that can well capture the
large spatial and spectral variability that is characteristic to satellite
imagery. By focusing on preserving locality, the proposed method is able to
decompose the weight-space of pre-trained GANs and recover interpretable
directions that correspond to high-level semantic concepts (such as
urbanization, structure density, flora presence) - that can subsequently be
used for guided synthesis of satellite imagery. In contrast to typically used
approaches that focus on capturing the variability of the weight-space in a
reduced dimensionality space (i.e., based on Principal Component Analysis,
PCA), we show that preserving locality leads to vectors with different angles,
that are more robust to artifacts and can better preserve class information.
Via a set of quantitative and qualitative examples, we further show that the
proposed approach can outperform both baseline geometric augmentations, as well
as global, PCA-based approaches for data synthesis in the context of data
augmentation for satellite scene classification.