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How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification by Jamali, Ali; Roy, Swalpa Kumar; Hong, Danfeng; Lu, Bing; Ghamisi, Pedram is a scholarly article available to read on EtoBox.

What is How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification about?

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 datas

Author

Jamali, Ali; Roy, Swalpa Kumar; Hong, Danfeng; Lu, Bing; Ghamisi, Pedram

Published

2024

Language

EN