使用Landsat-5进行准确的历史LULC分类:机器学习模型的比较

IF 2.7 3区 物理与天体物理 Q2 PHYSICS, ATOMIC, MOLECULAR & CHEMICAL
D. Krivoguz, S. Chernyi, Elena Zinchenko, Artem Silkin, A. Zinchenko
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引用次数: 1

摘要

本研究探讨了各种机器学习模型在刻赤半岛土地利用和土地覆盖(LULC)分类中的应用。该研究利用档案现场数据、地籍数据和已发表的科学文献进行模型训练和测试,使用1990年的Landsat-5图像作为输入数据。采用深度神经网络、随机森林、支持向量机(SVM)和AdaBoost四种机器学习模型,并通过随机搜索和网格搜索对其超参数进行调优。通过交叉验证和混淆矩阵来评估模型的性能。深度神经网络达到了最高的准确率(96.2%),在水、城市土地、开阔土壤和高植被分类方面表现良好。然而,它在划分草原、裸地和农业区方面面临挑战。随机森林模型的准确率为90.5%,但在区分高植被和农田方面存在困难。SVM模型的准确率为86.1%,而AdaBoost模型的准确率最低,为58.4%。本研究的新贡献包括对刻赤半岛土地利用分类的多种机器学习模型的比较和评估。深度神经网络和随机森林模型在精度方面优于SVM和AdaBoost。然而,使用有限的数据来源,如地籍数据和科学论文,可能会带来局限性和潜在的错误。未来的研究应考虑纳入实地研究和其他数据来源,以提高准确性。该研究为刻赤半岛的土地利用分类、自然资源评估和管理提供了有价值的见解。这些发现有助于知情的决策过程,并为该领域的进一步研究奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models
This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.
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来源期刊
Atomic Data and Nuclear Data Tables
Atomic Data and Nuclear Data Tables 物理-物理:核物理
CiteScore
4.50
自引率
11.10%
发文量
27
审稿时长
47 days
期刊介绍: Atomic Data and Nuclear Data Tables presents compilations of experimental and theoretical information in atomic physics, nuclear physics, and closely related fields. The journal is devoted to the publication of tables and graphs of general usefulness to researchers in both basic and applied areas. Extensive ... click here for full Aims & Scope Atomic Data and Nuclear Data Tables presents compilations of experimental and theoretical information in atomic physics, nuclear physics, and closely related fields. The journal is devoted to the publication of tables and graphs of general usefulness to researchers in both basic and applied areas. Extensive and comprehensive compilations of experimental and theoretical results are featured.
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