Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

中文翻译：

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无人机和地面遥感平台用于高通量表型分析的新正射影像生成策略

遥感平台已成为数字农业的有效数据采集工具。无人机和拖拉机上的成像传感器正在为几种作物表型活动（例如，冠层覆盖率估算，植物定位和开花日期识别）提供空前的高几何分辨率数据。在潜在产品中，正射影像在农业管理中发挥着重要作用。传统的正射影像生成策略会遭受多种伪影（例如，双重映射，过多像素化和接缝线失真）。在处理通常用于确定开花日期的中后期影像时，上述问题更为明显（例如，分别针对玉米和高粱作物的穗和穗状花序检测）。为了应对这些挑战，本文介绍了生成正射影像的新策略，这些策略有助于直接检测流苏和穗。正射影像生成策略对帧和推扫式成像系统均有效。这些策略的目标功能是在改善流苏/穗的视觉外观与其地理位置精度之间取得平衡。新策略基于生成平滑的数字表面模型（DSM）的功能，该模型可沿植物行保持地理位置质量，同时减少重复映射和像素化伪影。而且，采用了接缝线控制策略以避免在期望流苏和穗的位置出现接缝线变形。通过目视检查以及对生成的正射影像与原始图像之间相似度的定量评估，可以评估生成的正射影像的质量。无人机和地面平台的一些实验结果表明，所提出的策略确实改善了派生正射影像的视觉质量，同时保持了流苏/穗位置的地理位置精度。