Lightweight Remote Sensing Image Change Caption with Hierarchical Distillation and Dual-Constrained Attention

Remote sensing image change captioning (RSICC) fuses computer vision and natural language processing to translate visual differences between bi-temporal remote sensing images into interpretable text, with applications in environmental monitoring, urban planning, and disaster assessment. Multimodal Large Language Models (MLLMs) boost RSICC performance but suffer from inefficient inference due to massive parameters, whereas lightweight models enable fast inference yet lack generalization across diverse scenes, which creates a critical timeliness-generalization trade-off. To address this, we propose the Dual-Constrained Transformer (DCT), an end-to-end lightweight RSICC model with three core modules and a decoder. Full-Level Feature Distillation (FLFD) transfers hierarchical knowledge from a pre-trained Dinov3 teacher to a Generalizable Lightweight Visual Encoder (GLVE), enhancing generalization while retaining compactness. Key Change Region Adaptive Weighting (KCR-AW) generates Region Difference Weights (RDW) to emphasize critical changes and suppress backgrounds. Hierarchical encoding and Difference weight Constrained Attention (HDC-Attention) refine multi-scale features via hierarchical encoding and RDW-guided noise suppression; these features are fused by multi-head self-attention and fed into a Transformer decoder for accurate descriptions. The DCT resolves three core issues: lightweight encoder generalization, key change recognition, and multi-scale feature-text association noise, achieving a dynamic balance between inference efficiency and description quality. Experiments on the public LEVIR-CC dataset show our method attains SOTA among lightweight approaches and matches advanced MLLM-based methods with only 0.98% of their parameters.