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The application of deep learning algorithms to Earth observation (EO) inrecent years has enabled substantial progress in fields that rely on remotelysensed data. However, given the data scale in EO, creating large datasets withpixel-level annotations by experts is expensive and highly time-consuming. Inthis context, priors are seen as an attractive way to alleviate the burden ofmanual labeling when training deep learning methods for EO. For someapplications, those priors are readily available. Motivated by the greatsuccess of contrastive-learning methods for self-supervised featurerepresentation learning in many computer-vision tasks, this study proposes anonline deep clustering method using crop label proportions as priors to learn asample-level classifier based on government crop-proportion data for a wholeagricultural region. We evaluate the method using two large datasets from twodifferent agricultural regions in Brazil. Extensive experiments demonstratethat the method is robust to different data types (synthetic-aperture radar andoptical images), reporting higher accuracy values considering the major croptypes in the target regions. Thus, it can alleviate the burden of large-scaleimage annotation in EO applications.

Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery