Multi-Camera Imaging System for UAV Photogrammetry

Wierzbicki, Damian

doi:10.3390/s18082433

Cited by 55 publications

(37 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aerial imaging, as applied in this study, may also be limited in capturing significantly occluded panicles [75]. The value of applying both nadir and oblique has been demonstrated in other UAS-based studies [76][77][78]. In our case multi-angle images should provide better view of panicles and likely lead to better detection.…”

Section: Discussionmentioning

confidence: 78%

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Malambo

Popescu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.

show abstract

Section: Discussionmentioning

confidence: 78%

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Malambo

Popescu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The lens corrections applied to the fisheye GoPro images, although seeming somewhat drastic ( Figure 6), were effective, resulting in an Agisoft model relatively free of distortion ( Figure 8). Our results show that distorted source imagery can be addressed by simple tools that are not labor-intensive (e.g., Hastedt et al, 2016;Wierzbicki, 2018). The Agisoft model built from the corrected imagery was of sufficient quality to permit quantitative comparison with a model generated by a different camera.…”

Section: Effective Efficient Methodology For Quantitative Repeat Phomentioning

confidence: 84%

“…Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enough to be carried by off-the-shelf consumer drones, offer good resolution for their size, and can image large areas at relatively low altitudes (where lower wind speeds make UAV operation easier) [58,[61][62][63][64]. However, ultra-wide-angle lenses require substantial calibration and correction to correct the inherent barrel distortion [33,59]. In this study, we demonstrate that heavily distorted imagery captured with GoPro cameras can be effectively corrected with consumer software, and used to build high-resolution SfM models with little residual distortion.…”

mentioning

confidence: 99%

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Nagle-McNaughton

Cox

2019

Remote Sensing

View full text Add to dashboard Cite

Repeat photogrammetry is increasingly the go-too tool for long-term geomorphic monitoring, but quantifying the differences between structure-from-motion (SfM) models is a developing field. Volumetric differencing software (such as the open-source package CloudCompare) provides an efficient mechanism for quantifying change in landscapes. In this case study, we apply this methodology to coastal boulder deposits on Inishmore, Ireland. Storm waves are known to move these rocks, but boulder transportation and evolution of the deposits are not well documented. We used two disparate SfM data sets for this analysis. The first model was built from imagery captured in 2015 using a GoPro Hero 3+ camera (fisheye lens) and the second used 2017 imagery from a DJI FC300X camera (standard digital single-lens reflex (DSLR) camera); and we used CloudCompare to measure the differences between them. This study produced two noteworthy findings: First, volumetric differencing reveals that short-term changes in boulder deposits can be larger than expected, and that frequent monitoring can reveal not only the scale but the complexities of boulder transport in this setting. This is a valuable addition to our growing understanding of coastal boulder deposits. Second, SfM models generated by different imaging hardware can be successfully compared at sub-decimeter resolution, even when one of the camera systems has substantial lens distortion. This means that older image sets, which might not otherwise be considered of appropriate quality for co-analysis with more recent data, should not be ignored as data sources in long-term monitoring studies.Despite these improvements in protocols for data collection and analysis, quantifying change via repeat photogrammetry remains a challenge, generally requiring manipulation of digital terrain models (DTMs) using Geographic Information Systems (GIS) [45,46]. But recent innovations in 3D differencing software, as implemented in the open-source package CloudCompare (www.danielgm.net/cc/) enable rapid, objective, and replicable quantitative differencing [47]. CloudCompare is a cross-platform 3D point cloud and mesh processing package, which provides a suite of features for direct comparison of dense point clouds (>10 million points) and meshes on standard consumer computer hardware [48,49]. Accessibility and ease of use make CloudCompare an attractive tool, and it is applied increasingly in a variety of fields, including mapping [50,51], archaeology [52,53], and geomorphology [54][55][56][57].Another issue relevant to long term monitoring studies is how to deal with repeat imagery captured using different kinds of hardware, with different resolutions and/or distortion levels. The ultra-wide-angle (fisheye) lenses mounted on the popular consumer camera brand GoPro capture a~120 • field of view, but have barrel distortion due to their short focal length. Despite their limitations, GoPro cameras are popular tools for acquiring UAV imagery [24,31,[58][59][60], they are relatively inexpensive, are light enou...

show abstract

“…Because of low flight altitude and narrow FOVs of cameras equipped on UAVs, UAV-RS often acquires images with small ground coverage area, resulting in the increase of image amount, flight lines, flight cost and data collection time [59].…”

Section: Combined Field Of Viewmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

149

View full text Add to dashboard Cite

The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

show abstract

Multi-Camera Imaging System for UAV Photogrammetry

Cited by 55 publications

References 54 publications

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Measuring Change Using Quantitative Differencing of Repeat Structure-From-Motion Photogrammetry: The Effect of Storms on Coastal Boulder Deposits

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Contact Info

Product

Resources

About