  It has been verified that deep learning methods, including convolutional
neural networks (CNNs), graph neural networks (GNNs), and transformers, can
accurately extract features from hyperspectral images (HSIs). These algorithms
perform exceptionally well on HSIs change detection (HSIs-CD). However, the
downside of these impressive results is the enormous number of parameters,
FLOPs, GPU memory, training and test times required. In this paper, we propose
an spectral Kolmogorov-Arnold Network for HSIs-CD (SpectralKAN). SpectralKAN
represent a multivariate continuous function with a composition of activation
functions to extract HSIs feature and classification. These activation
functions are b-spline functions with different parameters that can simulate
various functions. In SpectralKAN, a KAN encoder is proposed to enhance
computational efficiency for HSIs. And a spatial-spectral KAN encoder is
introduced, where the spatial KAN encoder extracts spatial features and
compresses the spatial dimensions from patch size to one. The spectral KAN
encoder then extracts spectral features and classifies them into changed and
unchanged categories. We use five HSIs-CD datasets to verify the effectiveness
of SpectralKAN. Experimental verification has shown that SpectralKAN maintains
high HSIs-CD accuracy while requiring fewer parameters, FLOPs, GPU memory,
training and testing times, thereby increasing the efficiency of HSIs-CD. The
code will be available at https://github.com/yanhengwang-heu/SpectralKAN.
