  The application of deep learning algorithms to Earth observation (EO) in
recent years has enabled substantial progress in fields that rely on remotely
sensed data. However, given the data scale in EO, creating large datasets with
pixel-level annotations by experts is expensive and highly time-consuming. In
this context, priors are seen as an attractive way to alleviate the burden of
manual labeling when training deep learning methods for EO. For some
applications, those priors are readily available. Motivated by the great
success of contrastive-learning methods for self-supervised feature
representation learning in many computer-vision tasks, this study proposes an
online deep clustering method using crop label proportions as priors to learn a
sample-level classifier based on government crop-proportion data for a whole
agricultural region. We evaluate the method using two large datasets from two
different agricultural regions in Brazil. Extensive experiments demonstrate
that the method is robust to different data types (synthetic-aperture radar and
optical images), reporting higher accuracy values considering the major crop
types in the target regions. Thus, it can alleviate the burden of large-scale
image annotation in EO applications.
