RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning

Remote Sensing Image Change Captioning (RSICC) aimsto describe changes between bi-temporal remote sensing images andholds significant research and application value. However, most exist-ing methods rely on conventional deep learning architectures, and thelimited model capacity constrains performance. Although large-modelpost-training techniques have achieved great success in general domains,their direct transfer to RSICC remains challenging due to data scarcityand the need for fine-grained change understanding. To address this, wepropose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data genera-tion paradigm, release the instruction dataset RSICI, and establish atask-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change repre-sentations and guide the model in perceiving and understanding tem-poral differences. In addition, we propose Dual-Negative PreferenceOptimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICPand further refine model performance. Extensive experiments validatethe superior capability of RSICCLLM, which achieves outstanding re-sults with only 7B parameters, surpassing models of substantially largerscales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.