<p>Satellite imagery provides society with data to solve a wide range of problems, including monitoring environmental changes or the detection of objects of interest, among many others. Anomaly detection has gained attention in recent years, as it can be used to address the aforementioned problems effectively. Traditional anomaly detection methods pose challenges, as they rely on labelled datasets, which are often scarce, expensive to obtain, and limited in capturing the full spectrum of real-world anomalies. To address these challenges, self-supervised learning has emerged as a powerful paradigm that leverages the usage of anomaly detection techniques on unlabelled data, allowing for cheaper solutions that can learn robust representations of known and unknown anomalies in many fields, including earth observation-related fields. In this study, we investigate state-of-the-art self-supervised anomaly detection methods and assess their performance on two widely used satellite imagery datasets: LandCover.ai, which focuses on land cover classification, and the High-Resolution Cloud Detection Dataset, which provides detailed cloud cover annotations. These datasets enable a comprehensive evaluation of anomaly detection effectiveness across diverse geospatial contexts. Nevertheless, existing state-of-the-art models often exhibit limitations, particularly in achieving a balanced trade-off between precision and recall, largely due to the scarcity of supervisory information inherent to the anomaly detection task. To address this challenge, we propose a novel ensemble self-supervised learning framework that integrates both low- and high-level abstraction objectives, thereby leveraging local and global feature representations to improve anomaly detection performance. Experimental results demonstrate that our approach consistently outperforms existing self-supervised methods, achieving improvements in F1 score ranging from 2 to 10% across both datasets.</p>