D3PM: Dual-Stream Denoising Diffusion Probabilistic Model for Change Detection in Multimodal Remote Sensing Images

Detecting land cover changes from multi-temporal and multi-modal remote sensing images acquired by different sensors at the same location is a complex yet highly valuable task. Recently, diffusion models, exemplified by the Denoising Diffusion Probabilistic Model (DDPM), have garnered significant attention for their remarkable performance and straightforward architecture. These models excel in image generation, distribution modeling, and feature extraction, making them highly promising for advancing Multimodal Change Detection (MCD). In this paper, we propose a Dual-stream Denoising Diffusion Probabilistic Model (D3PM) to address the challenges of MCD. Specifically, D3PM leverages DDPM to design two distinct processing streams, one for each image modality. The first stream employs an unconditional DDPM, whose denoising encoder-decoder network can achieve robust feature extraction. The second stream employs a conditional DDPM to facilitate modal translation, enabling the extracted features to align with the characteristics of the other modality, thereby improving cross-modality comparability. To further enhance performance, we constructed a CD task branch based on the decoder features of the two DDPMs across multiple denoising time steps. Additionally, we designed a collaborative learning optimization strategy with asynchronous time steps, fostering cross-task knowledge sharing and mutual enhancement while preserving the integrity of individual task learning. Experimental results on multiple public datasets demonstrate the effectiveness and superiority of the proposed D3PM, which achieves efficient modal transformation and alignment, mitigates modal heterogeneity interference, and significantly improves detection performance.