Cycle-Based Frequency Disentanglement Diffusion Model With Self-Training for Cross-Domain Hyperspectral-RGB Change Detection.

Abstract

Hyperspectral images (HSI) change detection (CD) has become a powerful tool to analyze the sublte surface changes. However, the application of HSI CD is constrained by the limited availability of homogeneous HSIs. HSI-RGB multimodal CD address these limitations by collaboratively utilizing multi-source data. Although multimodal CD methods have achieved encouraging results, their performance often relies on the assumption that the training and test samples have similar distributions. Recently, some domain adaptive CD methods have been introduced. However, the additional modality differences in cross-domain multimodal CD pose challenges to existing domain adaptation techniques. To address these challenges, we propose a cycle-based frequency disentanglement diffusion model with self-training for cross-domain HSI-RGB multimodal CD, which explores a frequency-domain diffusion-driven self-training mechanism to enhance consistency in change representations across different modalities and domains. Specifically, a cyclic frequency domain disentanglement-based modality-domain alignment diffusion network is proposed to achieve modality and domain alignment within a unified diffusion framework. Subsequently, a curriculum-learning based self-training dual-domain CD network is designed to process the aligned images, which leverages pseudo-label reliability to ensure stable transfer of prior knowledge while exploits complementary features across modalities for collaborative CD. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in cross-domain multimodal CD tasks.