Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

Zheng, Mou‐Xiong; Zhang, Yongjun; Zhou, Shunping; Jian, Zhu; Xiong, Xiaozhen

doi:10.1016/j.cageo.2016.04.006

Cited by 14 publications

(5 citation statements)

References 15 publications

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“…This software uses Automatic Aerial Triangulation (AAT) and Bundle Block Adjustment (BBA) to accurately match georeferenced images [ 35 ]. AAT increases the accuracy of image stitching by using aerial triangulation as a serial execution process, while BBA optimizes the images for 3D model reconstruction [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Kazaz

Poddar

Arabi

et al. 2021

Sensors

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Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

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Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Kazaz

Poddar

Arabi

et al. 2021

Sensors

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“…In the field of computer vision, GPU‐accelerated bundle adjustment has been widely studied (Agarwal et al, 2010; Choudhary et al, 2010; Agullo et al, 2011; Wu et al, 2011; X. Liu et al, 2012; Hänsch et al, 2016; Zheng et al, 2017). The PCG method has been adopted by researchers such as Zheng et al (2016). In this study, a combined PCG and GPU computing method was used for the satellite image processing, to improve the calculation efficiency of the large‐scale block adjustment.…”

Section: Methodsmentioning

confidence: 99%

“…Liu et al, 2012;Hänsch et al, 2016;Zheng et al, 2017). The PCG method has been adopted by researchers such as Zheng et al (2016). In this study, a combined PCG and GPU computing method was used for the satellite image processing, to improve the calculation efficiency of the large-scale block adjustment.…”

Section: Optimal Selection Of Vcps With Planar Constraintsmentioning

confidence: 99%

Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery

Tong

Liu

et al. 2020

The Photogrammetric Record

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In large‐scale block adjustment of high‐resolution satellite imagery (HRSI) without ground control points (GCPs), the selection of virtual control points (VCPs) is an important factor in determining the block adjustment accuracy. The traditional VCP generation method is based on a regular grid and uses the initial rational polynomial coefficient (RPC) files but without considering topographic factors. To further improve the accuracy, this paper proposes an approach for the optimal selection of VCPs with planar constraints, which ensures that the VCPs are mostly in areas with small elevation differences and are not influenced by vegetation and buildings. A three‐step approach generates the 3D coordinates of tie points based on forward intersection, calculates their mean values and finally selects optimal VCPs using a threshold value. Experiments conducted using ZY3‐01 satellite images show the accuracy of the proposed method without GCPs outperforms the existing method.

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“…As an important means to improve positioning accuracy, block adjustment has been used in many stereo mappings [1][2][3][4][5][6][7][8]. There are many studies regarding the improvement of its method, such as the stable solution in free adjustment without GCPs [9][10][11][12], the storing and computing algorithm for large-scale data [13], the determination of appropriate weights for adjustment parameters [12], etc. These studies provide effective methods and thus improve accuracy or efficiency.…”

Section: Introductionmentioning

confidence: 99%

Geometric Accuracy Analysis of Regional Block Adjustment Using GF-7 Stereo Images without GCPs

Tang

Zhu

et al. 2023

Remote Sensing

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As an important means of improving positioning accuracy, block adjustment has been used in the improvement and assessment of accuracy for the Chinese Gaofen-7 (GF-7) satellite. However, there is little research on what factors affect accuracy without ground control points (GCPs). The correlation between accuracy and the images participating in the adjustment is not clear. This paper proposes the correlation coefficients and canonical correlation analysis between five accuracy indicators and three sets of ten adjustment factors, including topographic factors, participating image factors, and tie points (TPs) factors, to quantify the influence of adjustment factors on accuracy. Block adjustment without GCPs for GF-7 stereo imagery is verified in three study areas to evaluate the relationship between accuracy and adjustment factors. The results show that block adjustment without GCPs can improve direct positioning accuracy with an average improvement of 1.27 m in the planar direction and 0.13 m in the elevation direction. Moreover, plane accuracy is more easily affected by three sets of factors, while the influence on elevation accuracy is more balanced. The set of TP factors has the greatest influence on accuracy, and the image overlap is more critical than the image coverage area, number, and time periods. Topographic factors also play an important role, and the influence of the elevation factor with the highest canonical correlation coefficient (−0.71) is more significant than the other two factors, roughness, and slope. The results provide a reference for the improvement of adjustment accuracy without GCPs, the reasonable selection of adjustment images, the optimization of TPs, and the strategy of the partition processing of large-area block adjustment for GF-7 stereo imagery.

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Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

Cited by 14 publications

References 15 publications

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Optimal selection of virtual control points with planar constraints for large‐scale block adjustment of satellite imagery

Geometric Accuracy Analysis of Regional Block Adjustment Using GF-7 Stereo Images without GCPs

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