MFE-Net: Modal-Invariant Feature Extraction Network for Unsupervised Multimodal Change Detection

Unsupervised multimodal change detection (UMCD) has attracted significant attention due to its practical value and the main challenges it poses. 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 comparing features directly. Therefore, learning modal-invariant features that actively remove modal specificity while preserving the essential semantics of land use and land cover(LULC) is crucial. We propose a Modal-invariant Feature Extraction Network (MFE-Net) that learns semantics of LULC through two core modules: the Hybrid CNN-KAN (HCK) encoder and the Modal Difference Learning Module (MDLM). The HCK encoder provides a robust feature foundation for learning modal-invariant features. HCK combines convolutional neural networks (CNNs)' local perception with Kolmogorov-Arnold network (KAN)'s global nonlinear modeling to suppress modal noise and capture semantics, yielding lowmode-correlated features. The MDLM uses adversarial multi-task learning to explicitly separate modal-invariant components from mode-specific noise, ensuring that the HCK encoder generates features that are indistinguishable by modality of origin. To effectively leverage the extracted modal-invariant features to create a robust change map, we designed the Differential Feature Enhancement Module (DFEM) to calculate change intensity by fusing feature-level differences via Spearman's rank correlation. Experiments on six real datasets show MFE-Net outperforms state-of-the-art methods. Code and datasets are available at https://github.com/wangdetong-ctrl/MFE-Net.