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High-Precise True Digital Orthoimage Generation and Accuracy Assessment based on UAV Images

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Abstract

Unmanned aerial vehicles (UAVs) are modern tool for various scientific research areas such as agriculture, disaster management, mining and surveying. Among them, remote sensing is utilizing UAVs to acquire high-resolution digital images of ground surface. In remote sensing and photogrammetric operations, the geometric quality of the imagery basically depends on the relation between pixel size and the map scale, contrast information, atmosphere and the sun elevation, the printing technology, screen resolution and the visual acuity (ISPRS Int J Geo-Inf, Liu et al. Liu et al., ISPRS International Journal of Geo-Information 7:333, 2018; Eur Assoc Geosci Eng 1–5, Zatserkovnyi et al. Zatserkovnyi et al., European Association of Geoscientists and Engineers 25:1–5, 2020). In this study, accuracy has been assessed and compared of true digital orthoimages generated by two different software with same configuration. Three datasets of different places called dense urban, urban slum and forest mountains have been collected from field with area covering 0.061, 0.130 and 2.040 km2 and ground sampling distance 1.79, 2.19 and 11.86 cm, respectively. The orthoimages were generated using Pix4D and AgiSoft Photoscan software and then randomly distributed checkpoints, and visual angular distortion on the orthoimage was used to verify its precision. It was observed that in dense urban and urban slum Pix4D accuracy is more than AgiSoft Photoscan, but in forest mountain, it is vice versa. However, the process and mapping accuracy to generate TDOI require further improvement.

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Acknowledgement

The authors are very thankful to Professor Kamal Jain, Department of Civil Engineering, IIT Roorkee to provide necessary data, software and hardware support to conduct this study.

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Correspondence to Mohammad Shoab.

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Shoab, M., Singh, V.K. & Ravibabu, M.V. High-Precise True Digital Orthoimage Generation and Accuracy Assessment based on UAV Images. J Indian Soc Remote Sens 50, 613–622 (2022). https://doi.org/10.1007/s12524-021-01364-z

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  • DOI: https://doi.org/10.1007/s12524-021-01364-z

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