  Self-supervised learning aims to learn image feature representations without
the usage of manually annotated labels. It is often used as a precursor step to
obtain useful initial network weights which contribute to faster convergence
and superior performance of downstream tasks. While self-supervision allows one
to reduce the domain gap between supervised and unsupervised learning without
the usage of labels, the self-supervised objective still requires a strong
inductive bias to downstream tasks for effective transfer learning. In this
work, we present our material and texture based self-supervision method named
MATTER (MATerial and TExture Representation Learning), which is inspired by
classical material and texture methods. Material and texture can effectively
describe any surface, including its tactile properties, color, and specularity.
By extension, effective representation of material and texture can describe
other semantic classes strongly associated with said material and texture.
MATTER leverages multi-temporal, spatially aligned remote sensing imagery over
unchanged regions to learn invariance to illumination and viewing angle as a
mechanism to achieve consistency of material and texture representation. We
show that our self-supervision pre-training method allows for up to 24.22% and
6.33% performance increase in unsupervised and fine-tuned setups, and up to 76%
faster convergence on change detection, land cover classification, and semantic
segmentation tasks.
