Remote sensing image change captioning (RSICC) aims to generate semantic textual descriptions characterizing changes between bi-temporal remote sensing images, with wide applications in disaster assessment and urban planning. However, existing methods face specific drawbacks: CNN-based models have limited ability to capture long-range spatial correlations due to local receptive fields, and Transformer-based models suffer from quadratic complexity while distributing attention uniformly across all spatial positions, resulting in weak perception of salient changes in background-dominated scenes. In this paper, we present PM3Net (Progressive Mask-guided Multigranular Mamba Network), which leverages Mamba state space models with linear complexity for efficient spatiotemporal change modeling. The Progressive Mask-guided Encoder (PME) creates dual-source change masks combining L2 norm spatial differences with cosine distance semantic differences for progressive change feature extraction from detailed structures to high-level semantics. The Mask-guided Feature Enhancement (MFE) module applies mask-weighted refinement and cross-layer fusion to emphasize salient change regions while suppressing background interference, producing multigranular visual representations. Experiments on LEVIR-MCI and WHU-CDC datasets show PM3Net achieves superior results compared to existing methods, with BLEU-4 scores of 66.89 and 73.05, respectively. The results confirm PM3Net’s ability to solve the RSICC task while demonstrating how Mamba models can succeed in this specific field.
Qu et al. (Tue,) studied this question.