  Land cover classification and change detection are two important applications
of remote sensing and Earth observation (EO) that have benefited greatly from
the advances of deep learning. Convolutional and transformer-based U-net models
are the state-of-the-art architectures for these tasks, and their performances
have been boosted by an increased availability of large-scale annotated EO
datasets. However, the influence of different visual characteristics of the
input EO data on a model's predictions is not well understood. In this work we
systematically examine model sensitivities with respect to several color- and
texture-based distortions on the input EO data during inference, given models
that have been trained without such distortions. We conduct experiments with
multiple state-of-the-art segmentation networks for land cover classification
and show that they are in general more sensitive to texture than to color
distortions. Beyond revealing intriguing characteristics of widely used land
cover classification models, our results can also be used to guide the
development of more robust models within the EO domain.
