使用地理统计,终端提取和机器学习算法进行作物类型歧视
Prachi Singh1,2, Prashant K Srivastava1,2, Dharambhai Shah3
1Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India-221005.
Advances in space research : the official journal of the Committee on Space Research (COSPAR)
|January 22, 2024
概括
超光谱遥感 (HRS) 通过空中可见红外成像光谱仪新一代 (AVIRIS-NG) 数据准确识别作物类型. 2D-Convolutional神经网络 (CNN) 实现了89%的准确性,这对气候适应和粮食安全至关重要.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 频谱学是一种光谱学.
背景情况:
- 农作物多样性对于适应气候变化,农业生产力和粮食安全至关重要.
- 超光谱遥感 (HRS) 提供了一种强大的方法,可以使用详细的光谱信息来区分作物类型.
- 空载可见红外成像光谱仪-新一代 (AVIRIS-NG) 数据提供了高分辨率的光谱频段,用于先进的作物分析.
研究的目的:
- 探索和评估使用AVIRIS-NG数据进行作物分类和识别的技术.
- 创建不同作物类型的光谱库,以便进行准确的歧视.
- 为了比较各种监督分类器的作物识别性能.
主要方法:
- 利用AVIRIS-NG数据用于高光谱成像光谱学.
- 使用 Geo-Stat Endmember Extraction (GSEE) 算法来识别纯像素并创建一个光谱库.
- 来自AVIRIS-NG,ASD-Spectroradiometer和连续删除 (CR) 频谱的比较频谱特征.
- 应用了十个受监督的分类器,包括深度学习 (2D-CNN) 和整体方法,用于作物歧视.
- 使用整体准确度,卡帕系数,精度,回忆和F1分数评估分类器性能.
主要成果:
- 成功确定了九种作物类型:小麦,玉米,烟草,,亚麻种,子,豆,菜和小豆.
- 二维卷积神经网络 (2D-CNN) 分类器表现出卓越的性能.
- 使用2D-CNN实现了高性能指标:89.065%的整体准确率,0.871卡帕系数,87.565%的精度,89.541%的回忆率和88.678%的F1得分.
结论:
- 使用AVIRIS-NG数据和2D-CNN等先进分类器的超光谱遥感对于作物类型识别非常有效.
- 开发的方法允许在短时间内在物种层面准确,大规模地图作物作物.
- 这种方法支持农业监测,气候变化适应战略,并加强粮食安全工作.
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