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Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors by Wen Dai; Ruibo Qiu; Bo Wang; Wangda Lu; Guanghui Zheng; Solomon Obiri Yeboah Amankwah; Guojie Wang is a Environmental Science article available to read on EtoBox.
What is Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors about?
UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The “best practice” of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran’s I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran’s I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image bl
Who reads Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors?
It is typically read by researchers, students, and practitioners in Environmental Science.
- Author
- Wen Dai; Ruibo Qiu; Bo Wang; Wangda Lu; Guanghui Zheng; Solomon Obiri Yeboah Amankwah; Guojie Wang
- Publisher
- MDPI AG
- Published
- 2023
- Language
- EN
- Field
- Environmental Science (Physical Sciences)