Satellite Image Time-Series Data Augmentation Using an Attention Mechanism Variational Recurrent Autoencoder

Data scarcity presents a significant challenge in satellite image analysis, particularly for developing robust models in remote sensing applications. High-quality and abundant data are essential for accurate predictions; however, acquiring Satellite Image Time-Series (SITS) data is often constrained by factors such as limited temporal coverage and the high cost of Very High Resolution (VHR) acquisitions. To address this issue, we propose a novel Attention-based Variational Recurrent Autoencoder (AVRAE) designed for generating synthetic satellite image time-series data. This method extends the evidence lower bound (ELBO) of variational inference to incorporate the temporal dependencies essential for satellite data. A recurrent neural network-based autoencoder framework is employed, integrated with an attention mechanism to effectively capture both short- and long-term temporal relationships. The AVRAE framework synthesizes realistic and statistically representative satellite time-series data, enabling enhanced analysis for remote sensing applications. Evaluations using real-world satellite datasets demonstrate that AVRAE produces coherent and statistically valid synthetic data, thereby improving VHR SITS data quality for deep learning-based remote sensing applications.