Clustering augmented self-supervised learning: an application to land cover mapping

Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a significant obstacle for training advanced machine learning models. Standard techniques for addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering-based pretext task for self-supervised learning. We demonstrate the method's effectiveness in two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning, and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget for annotating. Finally, a real-world application of the developed framework in identifying intra-class categories of well-managed and poorly-managed plantations demonstrates its utility in a problem of considerable societal importance.