2020
DOI: 10.3390/rs12030351
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GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

Abstract: Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive ima… Show more

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Cited by 45 publications
(32 citation statements)
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“…Spatial and temporal system calibration for the datasets used in this study were conducted using the approaches described in [45] and [46], respectively. Additionally, the georeferenced orthomosaics were generated using the structure from motion strategies introduced in [47,48]. Visible near infrared (VNIR) and short wave infrared (SWIR) hyperspectral data were collected with two Headwall Photonics push-broom scanners.…”
Section: Remote Sensing Datamentioning
confidence: 99%
“…Spatial and temporal system calibration for the datasets used in this study were conducted using the approaches described in [45] and [46], respectively. Additionally, the georeferenced orthomosaics were generated using the structure from motion strategies introduced in [47,48]. Visible near infrared (VNIR) and short wave infrared (SWIR) hyperspectral data were collected with two Headwall Photonics push-broom scanners.…”
Section: Remote Sensing Datamentioning
confidence: 99%
“…In this study, an image-based sparse point cloud is generated through the GNSS/INS-assisted SfM strategy proposed by Hasheminasab et al [36]. This strategy takes advantage of the available GNSS/INS trajectory to facilitate the 3D reconstruction process and is conducted in four steps: stereo-image matching using the scale invariant feature transform (SIFT) [37] algorithm, relative orientation parameter (ROP) estimation, exterior orientation parameter (EOP) recovery, and bundle adjustment (BA).…”
Section: Image and Lidar-based Point Cloud Generationmentioning
confidence: 99%
“…More details regarding the first three steps of the implemented SfM framework can be found in [36,38].…”
Section: Image and Lidar-based Point Cloud Generationmentioning
confidence: 99%
See 1 more Smart Citation
“…In this paper, image-based sparse point cloud is generated through a GNSS/INS-assisted Structure from Motion (SfM) strategy introduced by Hasheminasab et al (2020). Then, dense point cloud is generated using an approach similar to the patchbased multi-view stereo (PMVS) algorithm (Furukawa, Ponce, 2009).…”
Section: Introductionmentioning
confidence: 99%