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Self-supervised learning aims to learn image feature representations withoutthe usage of manually annotated labels. It is often used as a precursor step toobtain useful initial network weights which contribute to faster convergenceand superior performance of downstream tasks. While self-supervision allows oneto reduce the domain gap between supervised and unsupervised learning withoutthe usage of labels, the self-supervised objective still requires a stronginductive bias to downstream tasks for effective transfer learning. In thiswork, we present our material and texture based self-supervision method namedMATTER (MATerial and TExture Representation Learning), which is inspired byclassical material and texture methods. Material and texture can effectivelydescribe any surface, including its tactile properties, color, and specularity.By extension, effective representation of material and texture can describeother semantic classes strongly associated with said material and texture.MATTER leverages multi-temporal, spatially aligned remote sensing imagery overunchanged regions to learn invariance to illumination and viewing angle as amechanism to achieve consistency of material and texture representation. Weshow that our self-supervision pre-training method allows for up to 24.22% and6.33% performance increase in unsupervised and fine-tuned setups, and up to 76%faster convergence on change detection, land cover classification, and semanticsegmentation tasks.

Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks