MFE-Net proposed for modal-invariant features in unsupervised multimodal change detection

[TechRxiv] Zhe Li, Detong Wang, Sheng Fang and co-authors present in a preprint a Modal-invariant Feature Extraction Network (MFE-Net) for unsupervised multimodal change detection (UMCD). According to the authors, aligning cross-modal images in a common subspace via deep learning without decoupling residual modal noise from intermediate features leads to poor alignment and discrimination errors when features are compared directly. MFE-Net learns land use and land cover semantics through a Hybrid CNN-KAN (HCK) encoder, which combines CNNs' local perception with Kolmogorov-Arnold network global nonlinear modeling, and a Modal Difference Learning Module (MDLM) that uses adversarial multi-task learning to separate modal-invariant components from mode-specific noise. A Differential Feature Enhancement Module (DFEM) computes change intensity by fusing feature-level differences via Spearman's rank correlation. The authors say experiments on six real datasets show MFE-Net outperforms state-of-the-art methods. The work is a preprint and has not been peer-reviewed.