Remote sensing change captioning (RSICC) aims to describe changes between bitemporal images in natural language. Existing methods often fail under challenges like illumination differences, viewpoint changes, blur effects, leading to inaccuracies, especially in no-change regions. Moreover, the images acquired at different spatial resolutions and have registration errors tend to affect the captions. To address these issues, we introduce SECOND-CC, a novel RSICC dataset featuring high-resolution RGB image pairs, semantic segmentation maps, and diverse real-world scenarios. SECOND-CC which contains 6,041 pairs of bitemporal RS images and 30,205 sentences describing the differences between images. Additionally, we propose MModalCC, a multimodal framework that integrates semantic and visual data using advanced attention mechanisms, including Cross-Modal Cross Attention (CMCA) and Multimodal Gated Cross Attention (MGCA). Detailed ablation studies and attention visualizations further demonstrate its effectiveness and ability to address RSICC challenges. Comprehensive experiments show that MModalCC outperforms state-of-the-art RSICC methods, including RSICCformer, Chg2Cap, and PSNet with +4.6% improvement on BLEU4 score and +9.6% improvement on CIDEr score. We will make our dataset and codebase publicly available to facilitate future research at https://github.com/ChangeCapsInRS/SecondCC

本研究针对遥感变化描述中存在的准确性问题，尤其是在不同的光照条件、视角变化和模糊影响下，提出了SECOND-CC数据集和MModalCC框架。SECOND-CC是一个高分辨率的RGB图像对的数据集，包含细致的语义分割图和30,205句描述，MModalCC通过融合语义和视觉数据，采用先进的注意机制显著提高了变化描述的准确性，验证实验表明其在指标BLEU4和CIDEr上分别提高了4.6%和9.6%。

遥感中的鲁棒变化描述：SECOND-CC数据集和MModalCC框架