Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions With LiDAR Data as Reference

Ni, Wenjian; Sun, Guoqing; Pang, Yong; Zhang, Zhiyu; Liu, Jianli; Yang, Aqiang; Wang, Yao; Zhang, Dafeng

doi:10.1109/jstars.2018.2867945

Cited by 41 publications

(38 citation statements)

References 27 publications

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“…Also, this study did not address the impact of changes in the image spatial resolution on the panicle detection. However, previous studies on UAS imaging performance have shown that lower resolution data tend to reduce the accuracy of derived metrics e.g., plant height and biomass [69][70][71]. Other studies have also shown that lower resolution images tend to lower the performance of deep learning models [72,73].…”

Section: Discussionmentioning

confidence: 97%

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

Malambo

Popescu

et al. 2019

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 97%

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

Malambo

Popescu

et al. 2019

Remote Sensing

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“…In such cases, the likelihood of identifying visual similarities in overlapping images is promoted by increasing the distance to the area of interest (AOI), thus increasing the number of features per image and decreasing perspective distortions. Coarser ground sampling distances (GSDs) and higher image overlaps were shown to positively influence image matching [95,96]. The overlap should thus be increased when decreasing the GSD (i.e.…”

Section: ) Image Matching Issuesmentioning

confidence: 99%

Structure from Motion Photogrammetry in Forestry: a Review

et al. 2019

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Purpose of Review The adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers. Recent Findings Our examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels. Summary We highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys. Keywords SfM. Point cloud. UAV. Close-range photogrammetry (CRP). Forest inventory. Forest health This article is part of the Topical Collection on Remote Sensing

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“…An analysis of forward overlap effects as associated with resolution or flight altitude was performed by Dandois et al [43], Frey et al [44] and Torres-Sánchez [45] whose showed that larger levels of forward overlap increased accuracy. Ni et al [46] also found that finer image resolutions required larger forward overlaps. Furthermore, Dandois et al [43] and Fraser et al [44] compared different processing algorithms, but small differences were revealed.…”

Section: Introductionmentioning

confidence: 96%

Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland

et al. 2019

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Unmanned aerial systems (UASs) and photogrammetric structure from motion (SFM) algorithms can assist in biomass assessments in tropical countries and can be a useful tool in local greenhouse gas accounting. This study assessed the influence of image resolution, camera type and side overlap on prediction accuracy of biomass models constructed from ground-based data and UAS data in miombo woodlands in Malawi. We compared prediction accuracy of models reflecting two different image resolutions (10 and 15 cm ground sampling distance) and two camera types (NIR and RGB). The effect of two different side overlap levels (70 and 80%) was also assessed using data from the RGB camera. Multiple linear regression models that related the biomass on 37 field plots to several independent 3-dimensional variables derived from five UAS acquisitions were constructed. Prediction accuracy quantified by leave-one-out cross validation increased when using finer image resolution and RGB camera, while coarser resolution and NIR data decreased model prediction accuracy, although no significant differences were observed in absolute prediction error around the mean between models. The results showed that a reduction of side overlap from 80 to 70%, while keeping a fixed forward overlap of 90%, might be an option for reducing flight time and cost of acquisitions. Furthermore, the analysis of terrain slope effect in biomass predictions showed that error increases with steeper slopes, especially on slopes greater than 35%, but the effects were small in magnitude.

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Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions With LiDAR Data as Reference

Cited by 41 publications

References 27 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

Structure from Motion Photogrammetry in Forestry: a Review

Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland

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