首页> 外文会议>Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology >Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived Digital Surface Maps
【24h】

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived Digital Surface Maps

机译:从无人驾驶飞行器(UAV)导出的数字表面图中提取的作物表面图的再现性

获取原文

摘要

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps(CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height fromthe UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flightconducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.
机译:从拥有市售的RGB摄像机配备的无人机测量的作物高度提供了一种实惠的替代方案,可以检索现场尺度高分辨率估计。该研究提出了由由运动(SFM)算法产生的数字表面图(DSM)中提取的裁剪表面图(CSM)的飞行重复性进行评估。在覆盖着苜蓿作物的中心枢轴灌溉系统上进行航班。计算UAV导出的DSM的绝对作物高度的重要一步是确定底层地形的高度。在这里,我们使用适用于RGB植被指数图的自动阈值化技术,以分类植被和土壤像素。根据分类土壤像素的插值,从DSM计算并减去地形图。研究了三种不同阈值技术对CSMS的影响。用不同阈值化方法确定的中位苜蓿裁剪高度从18厘米变化为K表示OTSU阈值方法的阈值为13cm。 OTSU阈值处理也给出了最小的作物高度范围,K表示最大的速度。飞行调查之间的中位作物高度的再现性为所有阈值技术为4-6厘米。对于下午后来的飞行,导致土壤像素被分类为域周围的关键位置中的植被,从而降低作物高度估计。两种航班的作物高度范围类似于使用K表示阈值平衡(35-36cm),局部最小阈值依赖于原始或标准化的RGB强度计算植被指数(30-35cm),而OTSU阈值均有较小的范围航班之间的高度和多样化(26-30厘米)。这项研究表明,来自多种调查飞行的作物高度是可比的,但是,它们依赖于应用于对土壤像素分类的阈值化方法以及在进行飞行的日期。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号