摘要
及时了解农作物空间分布信息,对于实现科学管理和作物增产等具有重要意义。无人机遥感系统因具有机动灵活、低成本、高效获取高分辨率影像等优点,在农业大面积遥感影像应用方面具有独特优势。针对无人机影像农田地块边界提取过程中,由于人工矢量化耗时费力和现有影像分割方法常见的过分割情况等带来的边界提取困难这一问题,提出了一种基于改进Mean Shift的无人机农业应用遥感影像地块边界自动化提取流程。根据地块本身的全局特点和地块内作物的局部特点,该方法综合考虑了像素点位置信息和影像颜色信息,配合空间带宽设置原则,描述地块边界特征。结果表明,基于改进Mean Shift的方法用于无人机农业应用遥感影像地块边界提取可以取得较好的结果,为其他学者开展农田地块边界研究提供了支持和启发。
Timely understanding of the spatial distribution of crops is of great significance for scientific management and crop increase.UAV remote sensing system has unique advantages in the application of large-area agricultural remote sensing images because of its advantages of mobility,low cost and high resolution image acquisition.To solve the problem of the difficulty of boundary extraction in UAV images,such as the time and effort of artificial vectoring and the common over-segmentation of existing image segmentation methods,an automated boundary extraction process based on improved Mean Shift is proposed.According to the global characteristics of the farmland itself and the local characteristics of the crops in the farmland,the method combines the pixel location information and image color information to describe the farmland boundary characteristics,in accordance with the spatial bandwidth setting principle.The experimental results show that the improved Mean Shift method proposed in this paper can achieve excellent results for land parcel boundary extraction from UAV agricultural remote sensing images,which provides support and inspiration for other scholars to carry out farmland parcel boundary research.
作者
郑明雪
罗治情
陈娉婷
官波
马海荣
ZHENG Ming-xue;LUO Zhi-qing;CHEN Pin-ting;GUAN Bo;MA Hai-rong(Institute of Agricultural Economic and Technological,Hubei Academy of Agricultural Sciences/Hubei Agricultural Science and Technology Innovation Center Agricultural Economic and Technological Research Sub-Center/Hubei Rural Revitalization Research Institute,Wuhan 430064,China)
出处
《湖北农业科学》
2021年第23期153-156,共4页
Hubei Agricultural Sciences
基金
湖北省农业科技创新中心项目(2019-620-000-001-29)。
