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This paper introduces the Dual-Selective Incremental Network (DSINet), a novel framework designed to address the challenges of domain-incremental change detection (DICD) by preserving spatial change representations across varying geographic domains. By utilizing a selective spatial state unit (S3U) and a concentration-balanced distillation (CBD) strategy, DSINet effectively filters domain-specific variations while stabilizing knowledge transfer, thus preventing feature confusion and knowledge degradation. Experimental results indicate that DSINet maintains linear computational efficiency while significantly improving performance across long domain sequences compared to existing methods.
DSINet prevents knowledge degradation in domain-incremental change detection, ensuring stable spatial representations even as geographic domains evolve.
Domain-incremental change detection (DICD) continuously adapts models to new geographic domains while preserving prior knowledge. However, a structural mismatch exists: the label space remains fixed while domain characteristics vary drastically. Consequently, incremental models struggle to maintain stable spatial change representations across domains. Existing strategies, such as replay-based or regularization-based methods, often fail to scale to long domain sequences, leading to knowledge degradation or increased computational cost. We propose Dual-Selective Incremental Network (DSINet), a unified framework built on visual state space models. DSINet leverages Mamba's input-dependent selective mechanism through a selective spatial state unit (S3U). This unit preserves stable spatial change structures while filtering domain-specific variations during feature propagation. As a result, spatial representations remain stable across domains, preventing the accumulation of feature confusion over incremental steps. Additionally, we employ a concentration-balanced distillation (CBD) strategy to stabilize knowledge transfer across domains. It balances hardness and confidence concentration effects during incremental updates. This ensures reliable probability mass allocation and prevents over-smoothing or mode collapse during distillation. Together, these mechanisms maintain stable learning dynamics throughout incremental stages. Experimental results demonstrate that DSINet mitigates knowledge degradation across long domain sequences while maintaining the linear computational efficiency of state space models.
