In this research, we propose the first approach for integrating the
Kolmogorov-Arnold Network (KAN) with various pre-trained Convolutional Neural
Network (CNN) models for remote sensing (RS) scene classification tasks using
the EuroSAT dataset. Our novel methodology, named KCN, aims to replace
traditional Multi-Layer Perceptrons (MLPs) with KAN to enhance classification
performance. We employed multiple CNN-based models, including VGG16,
MobileNetV2, EfficientNet, ConvNeXt, ResNet101, and Vision Transformer (ViT),
and evaluated their performance when paired with KAN. Our experiments
demonstrated that KAN achieved high accuracy with fewer training epochs and
parameters. Specifically, ConvNeXt paired with KAN showed the best performance,
achieving 94% accuracy in the first epoch, which increased to 96% and remained
consistent across subsequent epochs. The results indicated that KAN and MLP
both achieved similar accuracy, with KAN performing slightly better in later
epochs. By utilizing the EuroSAT dataset, we provided a robust testbed to
investigate whether KAN is suitable for remote sensing classification tasks.
Given that KAN is a novel algorithm, there is substantial capacity for further
development and optimization, suggesting that KCN offers a promising
alternative for efficient image analysis in the RS field.

本研究提出了一种名为 KCN 的新方法，将 Kolmogorov-Arnold Network（KAN）与各种预训练的卷积神经网络（CNN）模型结合，以用于利用 EuroSAT 数据集进行遥感场景分类任务，取得了高准确性且仅需少量训练轮数和参数，为遥感分类任务中的图像分析提供了一种高效的替代方案。