CD4C: Change Detection for Remote Sensing Image Change Captioning

Remote sensing image change captioning is an important image interpretation technique that automatically generates captions describing the visual changes in multitemporal remote sensing images. However, the visual changes present in multitemporal images can be classified as foreground changes, which are captured in captions, and background changes, which interfere with traditional methods and complicate the effective capture of foreground changes. This ultimately limits the overall performance of the model. To address this issue, this study introduces change detection for remote sensing image change captioning (CD4C). Specifically, a change detection module generates binary masks that contain relevant visual change information from multitemporal images. Subsequently, based on whether changes are detected, samples are classified and processed through the C-Stream and N-Stream of the multitemporal difference feature fusion (MDF) module to extract visual change features. The C-Stream leverages the visual change information provided by the mask to enhance the ability of CD4C to capture foreground visual change features at both the image and feature levels. The N-Stream incorporates a pseudofeature generation module designed to mitigate the interference caused by poor change detection results. Finally, the caption generation module interprets the visual change features extracted by the MDF to produce accurate textual descriptions. Experiments on the LEVIR-CC and Dubai-CC datasets demonstrate that the proposed method outperforms other approaches.