用柯尔莫哥洛夫网络做遥感土地分类，更省参数还更易懂。
Kolmogorov-Arnold Networks for Spatially Independent Multispectral Land Classification
- 用柯尔莫哥洛夫网络处理多光谱遥感数据，结构紧凑
- 在卡尔加里数据集上准确率媲美随机森林，优于MLP
- 参数少、可解释性强，适合资源受限场景
从卫星影像进行土地分类对土地管理、环境监测和城市规划至关重要。随机森林和多层感知机等机器学习方法在多光谱数据上表现良好，而柯尔莫哥洛夫网络作为一种新型紧凑架构也逐渐崭露头角。本研究评估了柯尔莫哥洛夫网络在兰德斯8号影像上的土地分类性能，并与随机森林和多层感知机模型进行对比。模型在阿尔伯塔省埃德蒙顿地区训练，在卡尔加里地区独立数据集上测试，涵盖农业、城市、水体、森林和裸地五类。在卡尔加里数据集上，柯尔莫哥洛夫网络的分类准确率与随机森林相当，优于多层感知机，同时显著减少可训练参数数量，并提供更强的可解释性。
原文摘要 · Abstract (English)
Land classification from satellite imagery is important for land management, environmental monitoring, and urban planning. Machine learning methods such as random forests and multilayer perceptrons have shown strong performance on multispectral data, while the Kolmogorov-Arnold network has emerged as an alternative architecture with compact model structures. This study evaluates the Kolmogorov-Arnold network for land classification using Landsat 8 imagery and compares it with random forest and multilayer perceptron models. The models were trained and tested on data from Edmonton, Alberta and evaluated on an independent dataset from Calgary, Alberta across five land classes: agriculture, urban, water, forest, and bare ground. For the Calgary dataset, the Kolmogorov-Arnold network matched the accuracy of the random forest and outperformed the multilayer perceptron, while requiring substantially fewer trainable parameters and providing greater interpretability.
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