Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas

In the evolving domain of built-up area surveillance, remote sensing technology emerges as an essential instrument for Change Detection (CD). The introduction of deep learning has notably augmented the precision and efficiency of CD. This study focuses on the integration of deep learning methodologies, specifically the diffusion model, into remote sensing CD tasks for built-up urban areas. The goal is to explore the potential of a pre-trained Text-to-Image Stable Diffusion model for CD tasks and propose a new model called the Difference Guided Diffusion Model (DGDM). DGDM incorporates multiple pre-training techniques for image feature extraction and introduces the Difference Attention Module (DAM) and an Image-to-Text (ITT) adapter to improve the correlation between image features and text semantics. Additionally, DGDM utilizes attention generated from pre-trained Denoise UNet to enhance CD predictions. The effectiveness of the proposed method is evaluated through comparative assessments on four datasets, demonstrating its superiority over previous deep learning methods and its ability to produce more precise and detailed CD results. This innovative approach offers a promising direction for future research in urban remote sensing, emphasizing the potential of diffusion models in enhancing urban CD precision and automation. Our implementation code is available at https://github.com/morty20200301/cd-diffusion.