印度多样化的景观中基于补丁的土地使用和土地覆盖数据集的生成和分类:机器学习和深度学习模型的比较研究
Nyenshu Seb Rengma1, Manohar Yadav2
1Geographic Information System (GIS) Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, Uttar Pradesh, India.
Environmental monitoring and assessment
|May 22, 2024
概括
这项研究使用Sentinel-2图像为印度创建了一个土地使用和土地覆盖 (LULC) 基准数据集. 卷积神经网络 (CNN) 模型的准确度超过90%,其中一个机器学习 (ML) 模型达到96%.
科学领域:
- 环境科学环境科学
- 遥感是一种远程传感.
- 计算机科学 计算机科学
背景情况:
- 土地使用和土地覆盖 (LULC) 分析对于环境和社会应用至关重要.
- 遥感 (RS) 数据促进了LULC分析,推动了对基准数据集的需求.
- 很少有LULC基准数据集,特别是在印度等不同地理环境中.
研究的目的:
- 为了解决印度缺乏LULC基准数据集的问题.
- 创建一个基于补丁的数据集,包含四个LULC类中的4000张标记为Sentinel-2图像.
- 评估和比较传统机器学习 (ML) 模型和卷积神经网络 (CNN) 对于LULC分类的性能.
主要方法:
- 开发一个新的LULC基准数据集,使用来自印度的Sentinel-2卫星图像.
- 实现了三种传统的ML模型和三种CNN用于图像分类.
- 对创建的数据集和现有的基准数据集的模型性能进行比较分析.
主要成果:
- 在LULC分类中,CNN模型在整体上始终达到很高的准确性 (>=90%).
- 一个ML模型表现出卓越的性能,达到96%的准确性,在这个特定的例子中表现优于CNN.
- 机器学习模型在现有数据集上显示了更高的预测准确性,其中LULC类较少.
结论:
- 开发的基准数据集为LULC分类的ML和CNN模型性能提供了有价值的见解.
- 对于LULC任务的模型选择应考虑任务的具体性,资源的可用性和性能效率的权衡.
- 该研究强调了ML和深度CNN模型在LULC分类环境中的优缺点.
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