• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Remote Sensing With Simulated Unmanned Aircraft Imagery for Precision Agriculture Applications
【24h】

Remote Sensing With Simulated Unmanned Aircraft Imagery for Precision Agriculture Applications

机译：模拟无人飞机图像的遥感技术在精密农业中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An important application of unmanned aircraft systems (UAS) may be remote-sensing for precision agriculture, because of its ability to acquire images with very small pixel sizes from low altitude flights. The objective of this study was to compare information obtained from two different pixel sizes, one about a meter (the size of a small vegetation plot) and one about a millimeter. Cereal rye () was planted at the Beltsville Agricultural Research Center for a winter cover crop with fall and spring fertilizer applications, which produced differences in biomass and leaf chlorophyll content. UAS imagery was simulated by placing a Fuji IS-Pro UVIR digital camera at 3-m height looking nadir. An external UV-IR cut filter was used to acquire true-color images; an external red cut filter was used to obtain color-infrared-like images with bands at near-infrared, green, and blue wavelengths. Plot-scale Green Normalized Difference Vegetation Index was correlated with dry aboveground biomass (), whereas the Triangular Greenness Index (TGI) was not correlated with chlorophyll content. We used the SamplePoint program to select 100 pixels systematically; we visually identified the cover type and acquired the digital numbers. The number of rye pixels in each image was better correlated with biomass (), and the average TGI from only leaf pixels was negatively correlated with chlorophyll content (). Thus, better information for crop requirements may be obtained using very small pixel sizes, but new algorithms based on computer vision are needed for analysis. It may not be necessary to geospatially register large numbers of photographs with very small pixel sizes. Instead, images could be anal- zed as single plots along field transects.
机译：无人机系统（UAS）的重要应用可能是精密农业的遥感，因为它能够从低空飞行中获取像素尺寸非常小的图像。这项研究的目的是比较从两种不同的像素大小中获得的信息，一种像素大小约为一米（一个小的植被图的大小），另一种像素大小约为一毫米。谷类黑麦（Beatsville Agriculture Research Center）种植了秋冬作物，并施用了秋肥和春肥，造成了生物量和叶片叶绿素含量的差异。通过将富士IS-Pro UVIR数码相机放在最低3米的高度来模拟UAS图像。使用外部UV-IR截止滤光片来获取真彩色图像。外部红色截止滤光片用于获得具有近红外，绿色和蓝色波长波段的彩色红外图像。地表尺度绿色归一化差异植被指数与干燥的地上生物量（）相关，而三角形绿色指数（TGI）与叶绿素含量不相关。我们使用SamplePoint程序系统地选择了100个像素；我们从视觉上识别了封面类型并获取了数字。每个图像中的黑麦像素数量与生物量相关性更好（），而仅叶像素的平均TGI与叶绿素含量呈负相关（）。因此，使用很小的像素大小可以获得更好的农作物需求信息，但是需要基于计算机视觉的新算法进行分析。可能不需要在地理空间上注册大量像素非常小的照片。取而代之的是，可以将图像分析为沿场横断面的单个图。

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