Adversarial Multitask Learning for Accurate Change Detection in Multi-Temporal Satellite Images

Change detection in multi-temporal satellite data is critical for climate resilience assessment. Despite significant advances in remote sensing technologies, a fundamental research gap persists in effectively handling irregularly collected satellite images, where varying acquisition intervals and atmospheric conditions compromise detection accuracy. This study addresses this gap by developing a robust methodology for identifying changed and unchanged regions in multi-temporal satellite images. Our objective is to create a change detection framework that detects changed and unchanged regions in satellite images having temporal inconsistencies and inherent class imbalance. We propose a novel multiview-multitask learning in change detection (MMLCD) approach that integrates information from both frequency and spatial domains. The frequency domain analysis captures global structural changes while minimizing the impact of temporal variations, whereas the spatial domain processing through a CNN encoder-decoder identifies fine-grained localized changes. We enhance this framework with adversarial training and develop a custom loss function to address the class imbalance, the imbalance between hard and easy samples, and the instability in adversarial training. The proposed method is validated on benchmark datasets collected from Landsat-8 over nine diverse locations and the Multi-Temporal Urban Development SpaceNet (MUDS) dataser; the result indicates the superior performance of MMLCD over several state-of-the-art. We applied our model to Sentinel-2 satellite imagery of Nioghalvfjerdsfjorden Glacier in Greenland to assess its robustness and practical applicability in real-world scenarios where reliable annotations are unavailable, and to validate our approach’s effectiveness in monitoring critical environmental processes. The implications of this research extend beyond technical advancement. It includes practical applications in disaster response, and sustainable development planning, offering policymakers more reliable tools for environmental monitoring and informed decision-making in the face of accelerating global changes.