Multimodal change detection (MCD) has important applications in disaster assessment, but the nonlinear distortion of features and spatial misalignment caused by sensor imaging differences make it difficult to obtain changes through direct comparison. To overcome the above problems, this study aims to realize MCD by capturing the modality-independent structural commonality features between Multimodal Remote Sensing Images (MRSIs). To achieve this, we devise a basic Graph Kolmogorov-Arnold Network (GKAN) to excavate spatial structural relationships and cross-modal nonlinear mappings simultaneously. Based on this, we propose a Dual-branch GKAN (DGKAN) for unsupervised MCD, which can capture spatial-spectral structural commonality features and compare them directly to detect changes. Concretely, the GKAN is used within the DGKAN to build two autoencoders consisting of a Siamese encoder and two independent decoders to learn spatial-spectral structural commonality features through feature reconstruction. Besides, we introduce a Covariance Structural Commonality Loss (CSCL), which guides the network in extracting spatial-spectral structural commonality features between MRSIs by unsupervised constraints on the distributional consistency of cross-modal features. Experiments on several MCD datasets show that the proposed DGKAN can achieve convincing results, and ablation studies verify the effectiveness of the GKAN and CSCL.

Multimodal change detection (MCD) has garnered significant interest due to its capacity to address a variety of emergencies in a timely and effective manner. However, discrepancies in sensors and imaging techniques often hinder the direct comparison of heterogeneous remote sensing images (HRSIs), making it difficult to extract change information. To overcome this challenge, we propose a novel superpixel-based AutoEncoder Kolmogorov-Arnold Network (AEKAN) for unsupervised MCD. The primary objective of AEKAN is to excavate the latent commonality features between HRSIs. Notably, commonality features in unchanged regions are generally more pronounced than those in changed regions, which can be leveraged to assess change magnitude. To achieve this, the proposed method utilizes the Kolmogorov-Arnold Network (KAN), renowned for its capability to model data distributions, to extract these commonality features between HRSIs. Concretely, the proposed AEKAN consists of a Siamese KAN encoder and dual KAN decoders. The Siamese encoder aims to map HRSIs and extract latent commonality features, while the dual decoders reconstruct original bitemporal images from these features. In addition, we incorporate a hierarchical commonality loss function within the Siamese encoder to train AEKAN. This loss function is designed to intentionally guide the network in capturing commonality features by minimizing the discrepancies in features extracted from HRSIs at each layer of the Siamese encoder. The extracted commonality features are then adopted to quantify the change magnitude between images through mean square error (MSE). Extensive experiments on five MCD datasets demonstrate that the proposed AEKAN outperforms existing methods. The source code is available at:https://github.com/TongfeiLiu/AEKAN-for-MCD.