GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

Hasheminasab, Seyyed Meghdad; Zhou, Tian; Habib, Ayman

doi:10.3390/rs12030351

Cited by 42 publications

(32 citation statements)

References 66 publications

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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%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

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Section: Remote Sensing Datamentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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“…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%

“…In the last step of the SfM framework, a GNSS/INS-assisted bundle adjustment is conducted to generate the final sparse point cloud using the modified collinearity equations [36] while involving the GNSS/INS trajectory and mounting parameters relating the GNSS/INS body frame to the camera frame. The camera distortions are based on the USGS SMAC distortion model where radial and decentering lens distortions are considered [32].…”

Section: Image and Lidar-based Point Cloud Generationmentioning

confidence: 99%

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LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

et al. 2020

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Unmanned aerial vehicles (UAVs) are quickly emerging as a popular platform for 3D reconstruction/modeling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such applications, LiDAR and frame cameras are the two most commonly used sensors for 3D mapping of the object space. For example, point clouds for the area of interest can be directly derived from LiDAR sensors onboard UAVs equipped with integrated global navigation satellite systems and inertial navigation systems (GNSS/INS). Imagery-based mapping, on the other hand, is considered to be a cost-effective and practical option and is often conducted by generating point clouds and orthophotos using structure from motion (SfM) techniques. Mapping with photogrammetric approaches requires accurate camera interior orientation parameters (IOPs), especially when direct georeferencing is utilized. Most state-of-the-art approaches for determining/refining camera IOPs depend on ground control points (GCPs). However, establishing GCPs is expensive and labor-intensive, and more importantly, the distribution and number of GCPs are usually less than optimal to provide adequate control for determining and/or refining camera IOPs. Moreover, consumer-grade cameras with unstable IOPs have been widely used for mapping applications. Therefore, in such scenarios, where frequent camera calibration or IOP refinement is required, GCP-based approaches are impractical. To eliminate the need for GCPs, this study uses LiDAR data as a reference surface to perform in situ refinement of camera IOPs. The proposed refinement strategy is conducted in three main steps. An image-based sparse point cloud is first generated via a GNSS/INS-assisted SfM strategy. Then, LiDAR points corresponding to the resultant image-based sparse point cloud are identified through an iterative plane fitting approach and are referred to as LiDAR control points (LCPs). Finally, IOPs of the utilized camera are refined through a GNSS/INS-assisted bundle adjustment procedure using LCPs. Seven datasets over two study sites with a variety of geomorphic features are used to evaluate the performance of the developed strategy. The results illustrate the ability of the proposed approach to achieve an object space absolute accuracy of 3–5 cm (i.e., 5–10 times the ground sampling distance) at a 41 m flying height.

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“…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%

Comparative Evaluation of Derived Image and Lidar Point Clouds From Uav-Based Mobile Mapping Systems

Zhou

Hasheminasab

Lin

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Unmanned aerial vehicles (UAVs) have been widely used for 3D reconstruction/modelling in various applications such as precision agriculture, coastal monitoring, and emergency management. For such mapping applications, camera and LiDAR are the two most commonly used sensors. Mapping with imagery-based approaches is considered to be an economical and effective option and is often conducted using Structure from Motion (SfM) techniques where point clouds and orthophotos are generated. In addition to UAV photogrammetry, point clouds of the area of interest can also be directly derived from LiDAR sensors onboard UAVs equipped with global navigation satellite systems/inertial navigation systems (GNSS/INS). In this study, a custom-built UAV-based mobile mapping system is used to simultaneously collect imagery and LiDAR data. Derived LiDAR and image-based point clouds are investigated and compared in terms of their absolute and relative accuracy. Furthermore, stability of the system calibration parameters for the camera and LiDAR sensors are studied using temporal datasets. The results show that while LiDAR point clouds demonstrate a high absolute accuracy over time, image-based point clouds are not as accurate as LiDAR due to instability of the camera interior orientation parameters.

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GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

Cited by 42 publications

References 66 publications

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

LiDAR-Aided Interior Orientation Parameters Refinement Strategy for Consumer-Grade Cameras Onboard UAV Remote Sensing Systems

Comparative Evaluation of Derived Image and Lidar Point Clouds From Uav-Based Mobile Mapping Systems

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