Synthetic Hyperspectral Images With Controllable Spectral Variability and Ground Truth

Spectral variability in hyperspectral images (HSIs) has received lot of attention over the last years, especially in the field of hyperspectral unmixing (HU) where it is a major issue. In this letter, we propose a method utilizing a variational autoencoder (VAE) for creating synthetic HSIs having controllable degree of spectral variability from existing HSIs with established ground-truth abundance maps and endmembers. Such synthetic datasets can be useful for developing HU methods that can handle spectral variability in HSIs. We investigate how the variability in the synthetic images differs from the original images and perform blind unmixing experiments using generated datasets to illustrate the effect of increasing variability. Code for method is available at
https://github.com/burknipalsson/vae_synthetic_hsi
.