  Hyperspectral image classification is a crucial but challenging task due to
the high dimensionality and complex spatial-spectral correlations inherent in
hyperspectral data. This paper employs Wavelet-based Kolmogorov-Arnold Network
(wav-kan) architecture tailored for efficient modeling of these intricate
dependencies. Inspired by the Kolmogorov-Arnold representation theorem, Wav-KAN
incorporates wavelet functions as learnable activation functions, enabling
non-linear mapping of the input spectral signatures. The wavelet-based
activation allows Wav-KAN to effectively capture multi-scale spatial and
spectral patterns through dilations and translations. Experimental evaluation
on three benchmark hyperspectral datasets (Salinas, Pavia, Indian Pines)
demonstrates the superior performance of Wav-KAN compared to traditional
multilayer perceptrons (MLPs) and the recently proposed Spline-based KAN
(Spline-KAN) model. In this work we are: (1) conducting more experiments on
additional hyperspectral datasets (Pavia University, WHU-Hi, and Urban
Hyperspectral Image) to further validate the generalizability of Wav-KAN; (2)
developing a multiresolution Wav-KAN architecture to capture scale-invariant
features; (3) analyzing the effect of dimensional reduction techniques on
classification performance; (4) exploring optimization methods for tuning the
hyperparameters of KAN models; and (5) comparing Wav-KAN with other
state-of-the-art models in hyperspectral image classification.
