An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Lim, P.; Rhee, Sooahm; Seo, Junghoon; Kim, Jae-In; Chi, Junhwa; Lee, Suk-bae; Kim, Taejung

doi:10.3390/rs13112118

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Shatangju tree. With the development of UAV technology, UAV images have also been widely used [49][50][51][52][53][54][55][56]. Studies [10][11][12][13] have shown that UAV tilt photogrammetry can quickly and conveniently obtain images and texture information of forests, pine trees, eucalyptus, etc.…”

Section: Discussionmentioning

confidence: 99%

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

Dong

Chen

et al. 2021

Remote Sensing

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Automatic acquisition of the canopy volume parameters of the Citrus reticulate Blanco cv. Shatangju tree is of great significance to precision management of the orchard. This research combined the point cloud deep learning algorithm with the volume calculation algorithm to segment the canopy of the Citrus reticulate Blanco cv. Shatangju trees. The 3D (Three-Dimensional) point cloud model of a Citrus reticulate Blanco cv. Shatangju orchard was generated using UAV tilt photogrammetry images. The segmentation effects of three deep learning models, PointNet++, MinkowskiNet and FPConv, on Shatangju trees and the ground were compared. The following three volume algorithms: convex hull by slices, voxel-based method and 3D convex hull were applied to calculate the volume of Shatangju trees. Model accuracy was evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results show that the overall accuracy of the MinkowskiNet model (94.57%) is higher than the other two models, which indicates the best segmentation effect. The 3D convex hull algorithm received the highest R2 (0.8215) and the lowest RMSE (0.3186 m3) for the canopy volume calculation, which best reflects the real volume of Citrus reticulate Blanco cv. Shatangju trees. The proposed method is capable of rapid and automatic acquisition for the canopy volume of Citrus reticulate Blanco cv. Shatangju trees.

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Section: Discussionmentioning

confidence: 99%

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

Dong

Chen

et al. 2021

Remote Sensing

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“…We used kappa values of the EOPs to recognize image's flight direction. (Lim et al, 2021) Figure 5 explains the concept of forming image strips from kappa values. Images were classified into image strips.…”

Section: Extraction Of Tie-points Within the Same Image Stripmentioning

confidence: 99%

Estimation of Sensor Offsets for a Uav Platform Using Tiepoints Only

Kim,

Lim,

Kim

2023

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

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Abstract. In a UAV system, physical sensor offsets occur between an observation sensor and a GPS/IMU sensor which represents the position of UAV. The difference of angle between the GPS/IMU sensor’s axis and the observation sensor’s axis is referred to as boresight angle. The difference in physical position between the two is referred to as lever-arm. It is important to obtain accurate offset values in order to utilize UAVs in rapid mapping manner. Due to the sensor offsets, misalignment error can be caused when generating a mosaic image. Offset values can be measured by using extensive ground control points. However, this is very costly and time-consuming. In this study, we describe an estimation method for sensor offsets using tie-points and re-weighted least square estimation method. The proposed method consists of 5 steps. Firstly, a frame images for the target area were classified into image strips based on the kappa value of initial EOPs (Exterior Orientation Parameters), after which tie points were extracted between adjacent images. Secondly, strip bundle adjustment was performed to update initial EOPs using images with the same flight direction. Thirdly, tie-points between adjacent strips were extracted. Fourthly, block bundle adjustment was performed in all images, using all extracted tie-points. Finally, a mosaic image is generated using the EOPs value to which the estimated sensor offset is applied. From this study, we confirmed that the sensor offset of the platform could be estimated only with the tie-points extracted between adjacent images. And we confirmed that misalignment was adjusted when generating mosaic image. We expect that our research makes UAV system to be operated more fluently.

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“…To reduce the number of image pairs, we group the overall images along their flight strips. Based on the horizontal translation of the initial orientations of images, the motion vectors of the UAV are determined as in our previous study [16].…”

Section: Tiepoint Extractionmentioning

confidence: 99%

Fast UAV Image Mosaicking by a Triangulated Irregular Network of Bucketed Tiepoints

Yoon,

Kim

2023

Remote Sensing

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To take full advantage of rapidly deployable unmanned aerial vehicles (UAVs), it is essential to effectively compose many UAV images into one observation image over a region of interest. In this paper, we propose fast image mosaicking using a triangulated irregular network (TIN) constructed from tiepoints. We conduct pairwise tiepoint extraction and rigorous bundle adjustment to generate rigorous tiepoints. We apply a bucketing algorithm to the tiepoints and generate evenly distributed tiepoints. We then construct a TIN from the bucketed tiepoints and extract seamlines for image stitching based on the TIN. Image mosaicking is completed by mapping UAV images along the seamlines onto a reference plane. The experimental results showed that the image mosaicking based on a TIN of bucketed tiepoints could produce image mosaics with stable and fast performance. We expect that our method could be used for rapid image mosaicking.

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An Optimal Image–Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images

Cited by 6 publications

References 21 publications

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

Estimation of Sensor Offsets for a Uav Platform Using Tiepoints Only

Fast UAV Image Mosaicking by a Triangulated Irregular Network of Bucketed Tiepoints

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