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.

该研究采用Wavelet-based Kolmogorov-Arnold Network (wav-kan) 架构来进行高光谱图像分类，利用可学习的小波函数作为激活函数，实现对输入光谱特征的非线性映射，达到有效地捕捉多尺度空间和谱域模式的目的。实验结果表明，该方法在分类性能上优于传统的多层感知器和基于样条曲线的模型，并提出了进一步验证其泛化能力、捕捉尺度不变特征、降维技术对分类性能的影响、优化方法以及与其他同类模型比较的未来工作。

揭示小波的威力: 一种基于小波的Kolmogorov-Arnold网络用于高光谱图像分类