Kolmogorov-Arnold Network for Hyperspectral Change Detection

Hyperspectral change detection (HCD) techniques to monitor Earth's surface processes advanced markedly in recent years. Seasonal variations and associated spectral signatures as well as nonlinear noise patterns emanating from sensors and atmospheric sources pose fundamental challenges in HCD. Advanced deep learning models, such as those that leverage convolutional neural networks (3D-Siamese) or transformers (MLP-Mixer), are increasingly employed to address these challenges. However, they often need substantial training data and computational resources. Here, we show that the Kolmogorov-Arnold network (KAN) can enhance HCD capabilities without the excessive training demand of deep networks. The Kolmogorov-Arnold theorem provides the theoretical foundation for our approach, which is particularly well-suited for hyperspectral data analysis by providing a rigorous basis for handling high-dimensional spectral signatures through dimensional reduction and feature extraction. Our architectural design employs this theoretical framework by incorporating specialized neural network layers that mirror the theorem's compositional structure, thereby facilitating efficient processing of spectral bands. By replacing the linear weighting scheme with learnable nonlinear functions, the Kolmogorov-Arnold network (KAN) provides a unique capability to capture intricate patterns and irregularities in high-dimensional data. Here, we compare five KAN-based architectures and deep learning models such as the MLP-Mixer, 3D-Siamese, dual-branch Siamese spatial-spectral Transformer attention network (DBS3TAN), and the Swin Transformer for HCD and show that the Chebyshev-KAN model, with an average overall accuracy of 97.35% over four real-world benchmark cases, outperforms other models while having a marked lower complexity than the deep learning models. We also show that the choice of fit nonlinear function and model structure is more important than the number of parameters in KAN-based models.