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首页> 外文会议>Autonomous air and ground sensing systems for agricultural optimization and phenotyping II >Automated Geographic Registration and Radiometric Correction for UAV-based Mosaics
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Automated Geographic Registration and Radiometric Correction for UAV-based Mosaics

机译：基于无人机的马赛克的自动地理配准和辐射校正

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Texas A&M University has been operating a large-scale, UAV-based, agricultural remote-sensing research project since 2015. To use UAV-based images in agricultural production, many high-resolution images must be mosaicked together to create an image of an agricultural field. Two key difficulties to science-based utilization of such mosaics are geographic registration and radiometric calibration. In our current research project, image files are taken to the computer laboratory after the flight, and semi-manual pre-processing is implemented on the raw image data, including ortho-mosaicking and radiometric calibration. Ground control points (GCPs) are critical for high-quality geographic registration of images during mosaicking. Applications requiring accurate reflectance data also require radiometric-calibration references so that reflectance values of image objects can be calculated. We have developed a method for automated geographic registration and radiometric correction with targets that are installed semi-permanently at distributed locations around fields. The targets are a combination of black (≈5% reflectance), dark gray (≈20% reflectance), and light gray (≈40% reflectance) sections that provide for a transformation of pixel-value to reflectance in the dynamic range of crop fields. The exact spectral reflectance of each target is known, having been measured with a spectrophotometer. At the time of installation, each target is measured for position with a real-time kinematic GPS receiver to give its precise latitude and longitude. Automated location of the reference targets in the images is required for precise, automated, geographic registration; and automated calculation of the digital-number to reflectance transformation is required for automated radiometric calibration. To validate the system for radiometric calibration, a calibrated UAV-based image mosaic of a field was compared to a calibrated single image from a manned aircraft. Reflectance values in selected zones of each image were strongly linearly related, and the average error of UAV-mosaic reflectances was 3.4% in the red band, 1.9% in the green band, and 1.5% in the blue band. Based on these results, the proposed physical system and automated software for calibrating UAV mosaics show excellent promise.
机译：德州农工大学自2015年以来一直在开展基于无人机的大规模农业遥感研究项目。要在农业生产中使用基于无人机的图像，必须将许多高分辨率图像拼接在一起以创建农业图像。领域。基于科学的此类镶嵌图利用的两个主要困难是地理配准和辐射校准。在我们当前的研究项目中，飞行后将图像文件带到计算机实验室，并对原始图像数据执行半手动预处理，包括正射镶嵌和辐射定标。地面控制点（GCP）对于镶嵌过程中图像的高质量地理配准至关重要。需要精确反射率数据的应用程序还需要进行辐射度校准参考，以便可以计算图像对象的反射率值。我们已经开发了一种自动地理注册和辐射校正的方法，该方法可以将目标永久固定在田野周围的分布式位置。目标是黑色（≈5％反射率），深灰色（≈20％反射率）和浅灰色（≈40％反射率）部分的组合，可在作物动态范围内将像素值转换为反射率领域。每个目标的精确光谱反射率是已知的，已使用分光光度计进行了测量。在安装时，使用实时运动GPS接收器测量每个目标的位置，以给出其精确的纬度和经度。为了精确，自动地进行地理配准，需要在图像中自动定位参考目标；自动辐射测定校准需要将数字自动转换为反射率。为了验证该系统的辐射校准，将经过校准的基于UAV的野外图像镶嵌与来自载人飞机的经过校准的单幅图像进行了比较。每个图像的选定区域中的反射率值呈线性关系，UAV马赛克反射率的平均误差在红色带中为3.4％，在绿色带中为1.9％，在蓝色带中为1.5％。基于这些结果，提出的用于校准无人机镶嵌图的物理系统和自动化软件显示出极好的前景。

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