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Hemispherical Directional Reflectance Factor using UAV and a hyperspectral camera,validation and crop field test

机译:使用无人机和高光谱相机的半球方向反射系数,验证和作物田间试验

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Small unmanned aerial vehicle (UAV) and a prototype hyperspectral imaging camera (HSI) was used to measure the hemispherical directional reflectance factor (HDRF) of a test field with known light scattering properties. The HSI acquires a burst of 24 images within two seconds and all of these images are acquired with different spectral content. By using the autopilot of the UAV, the flight can be preplanned so that the target area is optimally covered with overlapping images from multiple view angles. Structure from motion (SFM) algorithm is used to accurately determine the view angles for each image. The HDRF is calculated for each ground pixel by determining view directions from all of the images for that particular pixel. The pixel intensity values are then processed to reflectance by using a reference panel, which has been measured in laboratory with Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). The UAV flight was performed over a test field with different gravel targets. The targets have known HDRF and this allows us to validate the UAV results. Another test was performed over a crop field to display the potential of this method for crop monitoring.
机译:小型无人机(UAV)和原型高光谱成像相机(HSI)用于测量具有已知光散射特性的测试场的半球方向反射系数(HDRF)。 HSI在两秒钟内获取了24张图像的连拍,并且所有这些图像都是以不同的光谱含量获取的。通过使用无人机的自动驾驶仪,可以预先计划飞行,以便从多个视角以重叠的图像最佳地覆盖目标区域。运动结构(SFM)算法用于准确确定每个图像的视角。通过为每个特定像素确定所有图像的视图方向,可以为每个地面像素计算HDRF。然后,使用参考面板将像素强度值处理为反射率,该参考面板已在芬兰大地测量学院现场Goniospectrometer(FIGIFIGO)实验室中进行了测量。无人机飞行是在具有不同砾石目标的测试场上进行的。目标具有已知的HDRF,这使我们可以验证无人机的结果。在作物田间进行了另一项测试,以显示该方法在作物监测中的潜力。

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