  Two of the main challenges for cropland classification by satellite
time-series images are insufficient ground-truth data and inaccessibility of
high-quality hyperspectral images for under-developed areas. Unlabeled
medium-resolution satellite images are abundant, but how to benefit from them
is an open question. We will show how to leverage their potential for cropland
classification using self-supervised tasks. Self-supervision is an approach
where we provide simple training signals for the samples, which are apparent
from the data's structure. Hence, they are cheap to acquire and explain a
simple concept about the data. We introduce three self-supervised tasks for
cropland classification. They reduce epistemic uncertainty, and the resulting
model shows superior accuracy in a wide range of settings compared to SVM and
Random Forest. Subsequently, we use the self-supervised tasks to perform
unsupervised domain adaptation and benefit from the labeled samples in other
regions. It is crucial to know what information to transfer to avoid degrading
the performance. We show how to automate the information selection and transfer
process in cropland classification even when the source and target areas have a
very different feature distribution. We improved the model by about 24%
compared to a baseline architecture without any labeled sample in the target
domain. Our method is amenable to gradual improvement, works with
medium-resolution satellite images, and does not require complicated models.
Code and data are available.
