Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping

TL;DR

This paper presents a novel clustering-based self-supervised learning method for land cover mapping in remote sensing, addressing data annotation challenges and improving segmentation and classification accuracy.

Contribution

It introduces a new clustering-based pretext task for self-supervised learning tailored to remote sensing data, enhancing feature representation and mapping performance.

Findings

Improved segmentation accuracy on land cover datasets

Effective active sampling using cluster structures

Enhanced discriminative feature learning

Abstract

Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of 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 using any 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 effectiveness of the method on 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 of annotating.