CDPrompt: Multimodal Change Detection With In-Domain Prompt in Missing Modality Scenarios

The change detection aims to identify temporal changes in land cover. In emergency disaster scenarios, acquiring postchange optical images is often difficult due to factors such as adverse weather and illumination conditions. In contrast, the SAR-based change detection is robust to these environmental factors but is prone to speckle noise and often lacks clear semantic interpretation. These challenges highlight the importance of multimodal approaches that integrate the complementary information from different data sources. To address the domain gap between optical and SAR data, we propose change detection prompt (CDPrompt), an automatic prompt-learning framework that leverages in-domain change information as prompts to suppress fake changes caused by the domain gap between the two modalities. CDPrompt incorporates a modality-specific domain tuning module (DTM) to inject the domain knowledge into the segment anything model (SAM), enabling efficient adaptation to multimodal data with minimal labels and training costs. A low-level enhancement module (LwEM) further refines spatial details using historical optical images, while a consistency loss enhances the learning of domain-invariant features between prechange optical and SAR images. To support evaluation in disaster scenarios with missing modalities, we extend the DFC25 dataset and introduce the first disaster-oriented multimodal change detection dataset, DFC25-Extended, comprising DFC25-OS-S and DFC25-O-SO. Extensive experiments on the Onera Satellite Change Detection (OSCD) and DFC25-Extended datasets demonstrate the superior performance and practical value of CDPrompt. The code and dataset will be publicly available at: https://github.com/zhanglimeng13/CDPrompt