应用深度学习管道来从低质量的历史RGB图像中对土地覆盖进行分类
Harold N Eyster1,2, Brian Beckage1,2
1Department of Plant Biology, University of Vermont, Burlington, VT, United States of America.
PeerJ. Computer science
|June 10, 2024
概括
深度学习精确地从低质量的空中图像中对历史土地覆盖进行分类. 温哥华从1995年到2021年经历了显著的植被损失和增加的路面.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 准确的土地使用和土地覆盖 (LULC) 分类对于环境监测至关重要.
- 深度学习算法通过高分辨率数据提高了LULC分类的准确性.
- 描述历史土地覆盖面对于理解长期环境变化至关重要.
研究的目的:
- 开发和应用深度学习管道,以从低质量的航空图像中对历史土地覆盖进行分类.
- 评估1995年至2021年在加拿大温哥华的土地覆盖面变化.
- 证明深度学习对历史遥感数据分析的实用性.
主要方法:
- 使用了DeepLabv3+与一个状卷积神经网络架构.
- 在现代,高质量的卫星图像上训练模型,用于初始的土地覆盖分类.
- 使用手动注释和增强的历史RGB空中图像微调模型.
- 应用该模型来分类温哥华的历史土地覆盖面.
主要成果:
- 深度学习模型在分类历史土地覆盖方面取得了很高的准确性,与使用高质量图像的研究相美.
- 分析显示,在1995年至2021年期间,温哥华的植被覆盖面大幅减少.
- 具体的变化包括针叶树覆盖面的减少,路面的增加,以及树木和草地覆盖面的整体减少.
结论:
- 深度学习模型可以有效地对历史土地覆盖进行分类,即使是从退化图像.
- 开发的工作流程可以研究历史的土地覆盖动态和变化驱动因素.
- 这种方法可以应用于其他地区和时间段,以了解土地覆盖轨迹.
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