Adaptive Taylor Kolmogorov–Arnold Network for Hyperspectral Image Classification

Traditional deep learning models for hyperspectral image (HSI) classification are limited by static activation functions and linear weights, hindering the extraction of complex features effectively. The recent Kolmogorov–Arnold (KA) network, by introducing learnable activation functions, offers greater model flexibility, demonstrating potential to overcome the limitations of traditional models. However, the KA network still faces several challenges in practical applications, particularly in handling high-dimensional spatial-spectral data fusion, managing the complexities of B-spline functions, and meeting high-computational demands. To overcome these challenges, we propose the adaptive Taylor KA network (ATKAN) for HSI classification. ATKAN applies KA operations to channels while sliding across the spatial domain to process high-dimensional spatial-spectral features. It also employs the Taylor series instead of B-spline functions for activation functions, ensuring smooth approximations and enhancing the ability to model nonlinear patterns. In addition, ATKAN incorporates adaptive feature shifts along the spatial axis, channel grouping, and shuffling mechanisms to improve information processing efficiency and reduce computational consumption. By integrating these characteristics, ATKAN forms a shallow hybrid model capable of effectively handling complex linear and nonlinear spatial-spectral features. Experimental results on six HSI datasets show that ATKAN significantly enhances classification accuracy and efficiency compared to existing methods.