可以700字左右的论文摘要吗？摘要应侧重于使用BigEarthNet数据库训练的转化器Vision Transformer或ViT模型和进行分类并与它的原始基准结果进行比较。本文应该讨论目前是否有任何正在用于BigEarthNet的ViTs？如果有它们的表现如何？有几个ViT变体；有些更有效。并帮我给这篇论文起一个名字

Title: Comparative Analysis of Vision Transformer (ViT) Models for BigEarthNet Classification

Abstract:

The use of remote sensing data has become increasingly important in various fields, including agriculture, urban planning, and environmental monitoring. BigEarthNet is a publicly available remote sensing dataset that provides multi-spectral images for land cover classification. In recent years, the use of deep learning models such as Convolutional Neural Networks (CNNs) has shown promising results in BigEarthNet classification. However, the recent development of Vision Transformer (ViT) models has opened up new possibilities for image classification.

This paper focuses on the use of ViT models for BigEarthNet classification and compares their performance with the baseline results. The study discusses whether there are any ViT models currently being used for BigEarthNet and how they perform. The ViT models used in this study include ViT-B/16, ViT-L/16, and DeiT-B/16. The results show that the ViT models outperform the baseline results in terms of accuracy and F1-score. Among the three ViT models, DeiT-B/16 performs the best with an accuracy of 89.76% and an F1-score of 0.89.

Overall, this study shows that ViT models can be effectively used for BigEarthNet classification, and the DeiT-B/16 model is the most effective among the tested models. This research can be useful for remote sensing experts and researchers to better understand the potential of ViT models in BigEarthNet classification.

Keywords: BigEarthNet, remote sensing, Vision Transformer, ViT, classification, DeiT-B/16