Three-dimensional panoramic terrain reconstruction from aerial imagery

Ahua, Yang; Li, Xuejun; Xie, Jiancang; Wei, Yong

doi:10.1117/1.jrs.7.073497

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…The accuracy of DEM model is the core of this algorithm. There are mainly three ways to obtain DEM data: (1) Measuring on the ground by a hand-held GPS measuring instrument or total station; (2) Obtaining remote sensing images through photoelectric equipment and remote sensing technology, while carrying out three-dimensional reconstruction; (3) Using high-precision equipment such as radar and laser scanner to obtain elevation information like the 3D point cloud [65][66][67][68][69][70][71]. The accuracy of DEM model is the core of this algorithm.…”

Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

“…Wang [50] analyzed the imaging link of the aerial camera for photogrammetry of the target and established a multifactor positioning error analysis model. Furthermore, scholars have established corresponding mathematical models or correction algorithms for different error factors [63][64][65][66][67][68][69][70][71][72][73]. Taking the height error as an example, Qiao [63,64] has established the target positioning algorithm based on the digital elevation model, which solved the problem of excessive target height error caused by the positioning algorithm based on the earth ellipsoid model; HAN [72] used the feature points in the image to obtain height information and locate the target without terrain data, but this method needs to control the imaging distance and the identifiable feature points must be ensured to exist in the graph.…”

Section: Introductionmentioning

confidence: 99%

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Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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Aerial cameras are one of the main devices for obtaining ground images in the air. Since the industrial community sets higher requirements of aerial cameras’ self-locating performance yearly using aerial cameras to locate ground targets has become a research hotspot in recent years. Based on the situation that no ground control point exists in target areas, the calculation principle of the aerial remote sensing image positioning algorithm has been analyzed by establishing different positioning models. Several error analysis models of the positioning algorithm based on the total differential method and the Monte Carlo method are established, and relevant factors that cause the positioning error are summarized. The last section proposes the optimization direction of aerial camera positioning algorithms in the future, which are verified by related simulation experiments. This paper provides a certain degree of guidelines in this area for researchers, who can quickly understand the current development and optimization direction of target geo-location algorithms of aerial remote sensing imagery.

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Section: Target Location Algorithm Based On Digital Elevation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

et al. 2022

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“…Multi-view stereo can use redundant information to weaken the influence of occlusion and noise. From the Middlebury evaluation supplied by Seitz et al [34], for a single object or small-scale sense reconstruction, multi-view reconstruction can provide a first-rate result which is comparable to the point cloud obtained from laser scanning. Since the Structure from Motion (SFM) method makes it possible for disordered image calibration, multi-view stereo quickly extends from photogrammetric images to generic photos, even those downloaded from the Internet or captured from mobile phones [35].…”

Section: Multi-view Stereo In Computer Visionmentioning

confidence: 99%

A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

et al. 2016

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This paper presents a novel multi-view dense point cloud generation algorithm based on low-altitude remote sensing images. The proposed method was designed to be especially effective in enhancing the density of point clouds generated by Multi-View Stereo (MVS) algorithms. To overcome the limitations of MVS and dense matching algorithms, an expanded patch was set up for each point in the point cloud. Then, a patch-based Multiphoto Geometrically Constrained Matching (MPGC) was employed to optimize points on the patch based on least square adjustment, the space geometry relationship, and epipolar line constraint. The major advantages of this approach are twofold: (1) compared with the MVS method, the proposed algorithm can achieve denser three-dimensional (3D) point cloud data; and (2) compared with the epipolar-based dense matching method, the proposed method utilizes redundant measurements to weaken the influence of occlusion and noise on matching results. Comparison studies and experimental results have validated the accuracy of the proposed algorithm in low-altitude remote sensing image dense point cloud generation.

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“…Various remote sensing data and techniques have been used to construct DEMs, which include stereomapping, [10][11][12] LIDAR technique, 13,14 and interferometric synthetic aperture radar (InSAR). [15][16][17][18] The InSAR method can generate a precise DEM from the InSAR phase that is mainly contributed by the topographic height.…”

Section: Introductionmentioning

confidence: 99%

Application of ERS and Envisat cross-interferometry to generation and accuracy assessment of digital elevation model over northern Alaska

Lee

Jung

Lü

2015

J. Appl. Remote Sens

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The accuracy of a digital elevation model (DEM) generated from synthetic aperture radar (SAR) interferometry (InSAR) crucially depends on the length of the perpendicular baseline between SAR acquisitions. ERS-2 and Envisat cross-InSAR (CInSAR) are superior methods to create high precise DEM because the perpendicular baseline can be extended sufficiently long by compensating a slight difference in radar carrier frequency. We have assessed the accuracy of DEM generated by using ERS and Envisat satellite CInSAR techniques using the ice, cloud, and land elevation satellite global elevation data, which has an absolute vertical accuracy of about 2 cm. The study area is high flat land covered up with ice and snow in northern Alaska. Our result shows that the CInSAR-derived DEM can achieve an accuracy of about 0.50 m. This is much better than that of the National Elevation Dataset (DEM) (1.95 m) and is slightly lower than that of the airborne InSAR DEM (0.36 m).

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Three-dimensional panoramic terrain reconstruction from aerial imagery

Cited by 7 publications

References 3 publications

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points

A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

Application of ERS and Envisat cross-interferometry to generation and accuracy assessment of digital elevation model over northern Alaska

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