An Empirical Study on Data Augmentation for Pixelwise Satellite Image Time-Series Classification and Cross-Year Adaptation

Satellite image time series (SITS) are widely used for land cover mapping and vegetation monitoring. Despite the success of deep learning methods in SITS classification, their performance strongly depends on large labeled datasets. Data augmentation is a cost-effective strategy to prevent deep learning models from overfitting with limited labeled data, but its effectiveness for SITS has yet to be thoroughly explored. This paper provides an empirical study of 11 alternative augmentation techniques for pixelwise satellite time series, including noise injection, scaling, mixup, weighted dynamic time warping barycentric averaging, temporal dropout, window slicing, temporal shift, time warping, interpolation resampling, amplitude jittering, and phase jittering. Notably, interpolation resampling was introduced to handle irregularly sampled satellite time series, enhancing model robustness to data incompleteness and spatiotemporal heterogeneity. We evaluated the performance gains of different augmentation techniques and their combinations on both same-year and cross-year test data under varying conditions (sequence length, sample size, time period, parameter setting) and assessed their processing speeds. Based on the results, we summarized the conditions under which different augmentation techniques are effective and provided a systematic analysis of their performance. Our study offers practical guidance for data augmentation in various SITS classification applications.