ER-PASS: Experience Replay with Performance-Aware Submodular Sampling for Domain-Incremental Learning in Remote Sensing

In recent years, deep learning has become a dominant research trend in the field of remote sensing. However, due to significant domain discrepancies among datasets collected from various platforms, models trained on a single domain often struggle to generalize to other domains. In domain-incremental learning scenarios, such discrepancies often lead to catastrophic forgetting, hindering the practical deployment of deep learning models. To address this, we propose ER-PASS, an experience replay-based continual learning algorithm that incorporates a performance-aware submodular sampling strategy. ER-PASS balances adaptability across domains and retention of knowledge by combining the strengths of joint learning and experience replay, while maintaining practical efficiency in terms of training time and memory usage. We validated our method on two remote sensing applications—building segmentation and land use/land cover (LULC) classification—using UNet and DeepLabV3+. Experimental results show that ER-PASS consistently outperforms existing continual learning methods in average incremental accuracy (AIA) and backward transfer (BWT), ensuring generalization across domains and mitigating catastrophic forgetting. While these results were obtained under restricted conditions, limited to a sequence of domains from high to low resolution and two applications, they underscore the potential of ER-PASS as a practical and general-purpose solution for continual learning in remote sensing.