  Remote sensing image change captioning (RSICC) aims at generating human-like
language to describe the semantic changes between bi-temporal remote sensing
image pairs. It provides valuable insights into environmental dynamics and land
management. Unlike conventional change captioning task, RSICC involves not only
retrieving relevant information across different modalities and generating
fluent captions, but also mitigating the impact of pixel-level differences on
terrain change localization. The pixel problem due to long time span decreases
the accuracy of generated caption. Inspired by the remarkable generative power
of diffusion model, we propose a probabilistic diffusion model for RSICC to
solve the aforementioned problems. In training process, we construct a noise
predictor conditioned on cross modal features to learn the distribution from
the real caption distribution to the standard Gaussian distribution under the
Markov chain. Meanwhile, a cross-mode fusion and a stacking self-attention
module are designed for noise predictor in the reverse process. In testing
phase, the well-trained noise predictor helps to estimate the mean value of the
distribution and generate change captions step by step. Extensive experiments
on the LEVIR-CC dataset demonstrate the effectiveness of our Diffusion-RSCC and
its individual components. The quantitative results showcase superior
performance over existing methods across both traditional and newly augmented
metrics. The code and materials will be available online at
https://github.com/Fay-Y/Diffusion-RSCC.
