ArticleSensors (Basel, Switzerland)2024
HyperKAN: Kolmogorov-Arnold Networks Make Hyperspectral Image Classifiers Smarter.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Kolmogorov-Arnold Networks for Sensor Data Processing: A Comprehensive Survey of Architectures, Applications, and Open Challenges.Sensors (Basel, Switzerland) · 2026Review
- DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.Briefings in bioinformatics · 2025Article
- Classification of Mycena andSensors (Basel, Switzerland) · 2025Article
- A Deep Learning-Based Method for Bearing Fault Diagnosis with Few-Shot Learning.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
In traditional neural network designs, a multilayer perceptron (MLP) is typically employed as a classification block following the feature extraction stage. However, the Kolmogorov-Arnold Network (KAN) presents a promising alternative to MLP, offering the potential to enhance prediction accuracy. In this paper, we studied KAN-based networks for pixel-wise classification of hyperspectral images. Initially, we compared baseline MLP and KAN networks with varying numbers of neurons in their hidden layers. Subsequently, we replaced the linear, convolutional, and attention layers of traditional neural networks with their KAN-based counterparts. Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). Experiments conducted using seven publicly available hyperspectral datasets demonstrated a substantial improvement in classification accuracy across all the networks. The best classification quality was achieved using a KAN-based transformer architecture.
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Registered trials
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