沙丘形态分类和数据集构建方法基于无人驾驶飞行器的orthoimagery
Ming Li1, Zekun Yang1, Jiehua Yan1
1School of Energy and Transportation Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
卷积神经网络 (CNN) 模型使用无人机 (UAV) 图像准确地分类沙漠沙丘形态. 这项研究引入了一种有效的数据集构建方法,用于大规模的沙丘分类.
科学领域:
- 地质形态学 地质形态学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 沙丘形态分布对于理解沙漠特征和演变至关重要.
- 沙丘形态的传统手册分类是主观的,耗时的,且效率低下.
- 卷积神经网络 (CNN) 模型为自动分类提供先进的图像特征表示.
研究的目的:
- 开发沙漠沙丘形态的智能分类方法.
- 从无人机图像中构建沙丘形态数据集的有效方法.
- 在中国沙漠中发现的五种典型的沙丘形态的分类.
主要方法:
- 进行了实地调查,并收集了内蒙古五种典型的沙丘类型的无人机 (UAV) 整形图像.
- 使用手动细分,自动规则细分,随机选和数据增强构建了沙丘形态数据集.
- 采用VGG16和VGG19CNN模型进行沙丘形态分类,并使用各种评估指标分析结果.
主要成果:
- 使用VGG16实现了高分类性能,达到97.05%的准确性,96.91%的精度,96.76%的回忆率和96.82%的F1-Score.
- 通过1024 × 1024像素的正规细分尺度和无人机正形图像的100像素重叠,获得了最佳结果.
- 由自动分割的无人机正视图构建的拟议数据集构建方法对于大规模的沙丘分类是有效的.
结论:
- CNN模型,特别是VGG16,为自动化沙丘形态分类提供了强大而高效的方法.
- 开发的数据集构建方法为大规模地形学研究提供了有价值的参考.
- 这项研究推动了沙漠地形学和遥感应用中的智能分析.
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