Deep neural networks face several challenges in hyperspectral image
classification, including high-dimensional data, sparse distribution of ground
objects, and spectral redundancy, which often lead to classification
overfitting and limited generalization capability. To more efficiently adapt to
ground object distributions while extracting image features without introducing
excessive parameters and skipping redundant information, this paper proposes
KANet based on an improved 3D-DenseNet model, consisting of 3D KAN Conv and an
adaptive grid update mechanism. By introducing learnable univariate B-spline
functions on network edges, specifically by flattening three-dimensional
neighborhoods into vectors and applying B-spline-parameterized nonlinear
activation functions to replace the fixed linear weights of traditional 3D
convolutional kernels, we precisely capture complex spectral-spatial nonlinear
relationships in hyperspectral data. Simultaneously, through a dynamic grid
adjustment mechanism, we adaptively update the grid point positions of
B-splines based on the statistical characteristics of input data, optimizing
the resolution of spline functions to match the non-uniform distribution of
spectral features, significantly improving the model’s accuracy in
high-dimensional data modeling and parameter efficiency, effectively
alleviating the curse of dimensionality. This characteristic demonstrates
superior neural scaling laws compared to traditional convolutional neural
networks and reduces overfitting risks in small-sample and high-noise
scenarios. KANet enhances model representation capability through a 3D dynamic
expert convolution system without increasing network depth or width. The
proposed method demonstrates superior performance on IN, UP, and KSC datasets,
outperforming mainstream hyperspectral image classification approaches.

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