  Convolutional Neural Networks (CNNs) and vision transformers (ViTs) have
shown excellent capability in complex hyperspectral image (HSI) classification.
However, these models require a significant number of training data and are
computational resources. On the other hand, modern Multi-Layer Perceptrons
(MLPs) have demonstrated great classification capability. These modern
MLP-based models require significantly less training data compared to CNNs and
ViTs, achieving the state-of-the-art classification accuracy. Recently,
Kolmogorov-Arnold Networks (KANs) were proposed as viable alternatives for
MLPs. Because of their internal similarity to splines and their external
similarity to MLPs, KANs are able to optimize learned features with remarkable
accuracy in addition to being able to learn new features. Thus, in this study,
we assess the effectiveness of KANs for complex HSI data classification.
Moreover, to enhance the HSI classification accuracy obtained by the KANs, we
develop and propose a Hybrid architecture utilizing 1D, 2D, and 3D KANs. To
demonstrate the effectiveness of the proposed KAN architecture, we conducted
extensive experiments on three newly created HSI benchmark datasets:
QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the
competitive or better capability of the developed hybrid KAN-based model across
these benchmark datasets over several other CNN- and ViT-based algorithms,
including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT.
The code are publicly available at (https://github.com/aj1365/HSIConvKAN)
