Bundle Block Adjustment of Weakly Connected Aerial Imagery

Zhang, Yongjun; Xiong, Xiaozhen; Shen, Xiang; Ji, Zheng

doi:10.14358/pers.78.9.983

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…When the LiDAR strip errors are large due to the poor accuracy of the POS system, a large number of L-SGCTs are required, which is expensive and labor-intensive. On the contrary, some previous research proved that only a few P-SGCPs are needed to obtain satisfactory aerotriangulation accuracy for aerial images using POS-aided aerotriangulation [28], [29].…”

Section: Multifeatures Matching Between Aerial Imagery and Lidarmentioning

confidence: 95%

“…In fact, the systematic errors of the position observations and the angular observations of the POS system are time dependent in a single strip [28], and Kager uses time-dependent polynomials to correct POS observation values [34]. Therefore, as is shown in (6), second-order polynomials are adopted to fit the systematic errors of the laser scanner position and angular observations for a short period of time in this paper.…”

Section: Mathematical Model Of At-aided Lsamentioning

confidence: 99%

“…Aerotriangulation of the aerial images is performed using the current POSaided aerotriangulation method in [28] and [29]. Then, two major types of conjugate features are matched between the triangulated aerial images and the LiDAR strips.…”

Section: Multifeatures Matching Between Aerial Imagery and Lidarmentioning

confidence: 99%

“…If a few photogrammetry-specific ground control points (P-SGCPs) are measured, POS-aided aerotriangulation can be conducted to obtain the precise exterior orientation parameters of aerial images [28], [29]. Unlike the L-SGCTs, the P-SGCPs need not be specially manufactured and can be measured after the flight mission, and their optimum positions can be determined by checking the real overlap of the aerial images.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

LiDAR Strip Adjustment Using Multifeatures Matched With Aerial Images

Zhang

Xiong

Zheng

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Airborne light detecting and ranging (LiDAR) systems have been widely used for the fast acquisition of dense topographic data. Regrettably, coordinate errors always exist in LiDAR-acquired points. The errors are attributable to several sources, such as laser ranging errors, sensor mounting errors, and position and orientation system (POS) systematic errors, among others. LiDAR strip adjustment (LSA) is the solution to eliminating the errors, but most state-of-the-art LSA methods neglect the influence from POS systematic errors by assuming that the POS is precise enough. Unfortunately, many of the LiDAR systems used in China are equipped with a low-precision POS due to cost considerations. Subsequently, POS systematic errors should be also considered in the LSA. This paper presents an aerotriangulation-aided LSA (AT-aided LSA) method whose major task is eliminating position and angular errors of the laser scanner caused by boresight angular errors and POS systematic errors. The aerial images, which cover the same area with LiDAR strips, are aerotriangulated and serve as the reference data for LSA. Two types of conjugate features are adopted as control elements (i.e., the conjugate points matched between the LiDAR intensity images and the aerial images and the conjugate corner features matched between LiDAR point clouds and aerial images). Experiments using the AT-aided LSA method are conducted using a real data set, and a comparison with the three-dimensional similarity transformation (TDST) LSA method is also performed. Experimental results support the feasibility of the proposed AT-aided LSA method and its superiority over the TDST LSA method.Index Terms-Aerial image, corner feature, image matching, light detecting and ranging (LiDAR) intensity image, LiDAR strip adjustment (LSA).

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Section: Multifeatures Matching Between Aerial Imagery and Lidarmentioning

confidence: 95%

Section: Mathematical Model Of At-aided Lsamentioning

confidence: 99%

Section: Multifeatures Matching Between Aerial Imagery and Lidarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LiDAR Strip Adjustment Using Multifeatures Matched With Aerial Images

Zhang

Xiong

Zheng

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…Today RFM can suit almost all spaceborne optical and SAR images. Aiming at image positioning with weak convergence geometry, Zhang et al(2012) proposed an algorithm of bundle block adjustment with a weakly connected condition for aerial imagery. Tee-Ann Teo (Teo,2010) Use the digital elevation model (DEM) as the elevation control and indicate the method can significantly improve the geometric accuracy as well as the geometric discrepancies between images.…”

Section: General Instructionsmentioning

confidence: 99%

RFM-Based Block Adjustment for Spaceborne Images with Weak Convergent Geometry

Cheng

Zhang

Huang

2015

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

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ABSTRACT:Block adjustment is one of the most important processing steps in topographic mapping. In order to achieve precise 3D photogrammetry points, the ratio of base-to-height(RBH) or the forward intersection angle(FIA) of stereo images should meet the requirement of specifications for topographic maps when the block images are used. In some fields, overlaps and ratios of base-toheight are often ignored, no gaps among the images are the basic requirements for block images. In this study we give a method to detect the forward intersection angle for homonymous points with RPCs, and create a indicator angler to reflect the level of weak convergence. In order to achieve stable and precise solver for block adjustment under the condition of weak convergence, this paper take the elevation value interpolated from DEM as observed values with errors, combine the height value and measured image coordinates to build adjustment model. The model can be steady solved in ill-pose situation and have little effects from the DEM errors. the model and the methods are validated by simulation and real data including optical ZY1-02C and TerraSAR images.

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