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Evaluation and enhancement of unmanned aircraft system photogrammetric data quality for coastal wetlands
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2020-10-02 , DOI: 10.1080/15481603.2020.1819720
Sara Denka Durgan 1 , Caiyun Zhang 1 , Aaron Duecaster 1
Affiliation  

ABSTRACT Understanding the impacts of flight configuration and post-mission data processing techniques on unmanned aircraft system (UAS) photogrammetric data quality is essential for employing this popular technique in coastal wetland ecosystems. In this study, we systematically evaluated the effects of flight configuration (flying altitude, image overlap, and lighting conditions) on UAS photogrammetric level 1 products: orthoimagery and point clouds, and level 2 products: digital terrain models (DTM) and canopy height models (CHM). We also developed an object-based machine learning approach to correct UAS DTMs to mitigate data uncertainties caused by flight configuration and dense vegetation. Flying altitude was identified as the leading parameter in the quality of level 1 products, while image overlap was the most influential determinant for the quality of level 2 products. The correction approach effectively reduced the vertical error of DTMs for two study sites. This study informs UAS photogrammetric survey design and data enhancement for applications in coastal wetlands.

中文翻译:

滨海湿地无人机系统摄影测量数据质量评价与提升

摘要 了解飞行配置和任务后数据处理技术对无人机系统 (UAS) 摄影测量数据质量的影响对于在沿海湿地生态系统中采用这种流行技术至关重要。在本研究中,我们系统地评估了飞行配置(飞行高度、图像重叠和光照条件)对无人机摄影测量 1 级产品:正射影像和点云,以及 2 级产品:数字地形模型 (DTM) 和冠层高度模型的影响(CHM)。我们还开发了一种基于对象的机器学习方法来纠正 UAS DTM,以减轻由飞行配置和茂密植被引起的数据不确定性。飞行高度被确定为一级产品质量的主导参数,而图像重叠是决定 2 级产品质量的最有影响力的决定因素。校正方法有效地减少了两个研究站点的 DTM 的垂直误差。这项研究为 UAS 摄影测量设计和数据增强提供了在沿海湿地中的应用。
更新日期:2020-10-02
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