Registration of Optical Images with Lidar Data and Its Accuracy Assessment

Zheng, Shunyi; Huang, Rongyong; Zhou, Yang

doi:10.14358/pers.79.8.731

Cited by 20 publications

(20 citation statements)

References 18 publications

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“…This study is the first to establish a transformation model from high-sampling-rate multispectral waveform data to RGB values, thereby achieving the goal of obtaining color information only through active remote sensing without a camera. Previous studies have obtained the color values (R, G and B) by means of passive images [11][12][13]. Although these studies have achieved relatively high accuracy, geometric registration errors during active and passive fusion cannot be avoided, which needs considerable time and labor to compensate.…”

Section: Discussionmentioning

confidence: 99%

“…Since the passive imaging sensors rely on solar illumination, certain inevitable errors are caused by geometric registration between the active and passive datasets. Although many studies have focused on solving the geometric registration problems and achieved high registration accuracy [11][12][13], considerable time and labor are still needed to achieve a satisfactory registration effect.…”

Section: Introductionmentioning

confidence: 99%

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Color Restoration for Full-Waveform Multispectral LiDAR Data

et al. 2020

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The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R 2 ) and color difference (∆E) metrics suggest that the color restoration for the full-waveform multispectral data is feasible. (Figure 1b), the RGB bands, namely, 434.5-474.5, 517-537 and 612-644 nm, in the visible spectral portion were selected for the receiving channels of color information. In addition, the photo multiplier tube (PMT) selected as the detector should be more suitable for detecting laser echoes in visible bands than the avalanche photodiode especially in the relatively low-energy B band since PMT has higher quantum efficiency and gain. Moreover, both the transmitted pulse and receiving echo at RGB bands are full-waveform recorded so that the receiving echoes can be calibrated with the transmitted pulses to eliminate errors caused by laser output energy instability. The waveforms of the three receiving channels were digitized in a 12-bit analog to digital converter (SP Devices, ADQ412), with a 1.8 GHz sampling rate. The length of each active signal is set to 20 samples based on the laser pulse width of 2 ns and sampling rate, thus greatly reducing the amount of data. Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 19 photo multiplier tube (PMT) selected as the detector should be more suitable for detecting laser 96 echoes in visible bands than the avalanche photodiode especially in the relatively low-energy B band 97 since PMT has higher quantum efficiency and gain. Moreover, both the transmitted pulse and 98 receiving echo at RGB bands are full-waveform recorded so that the receiving echoes can be 99 calibrated with the transmitted pulses to eliminate errors caused by laser output energy instability. 100The waveforms of the three receiving channels were digitized in a 12-bit analog to digital converter 101 (SP Devices, ADQ412), with a 1.8 GHz sampling rate. The length of each active signal is set to 20 102 samples based on the laser pulse width of 2 ns a...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Restoration for Full-Waveform Multispectral LiDAR Data

et al. 2020

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“…In current related research work, reducing the impact of non-rigid distortion to the precision is only applied to simple scenes, such as close-range photogrammetry. Li et al [24] and Zheng et al [25], given the distance between the 3D feature point cloud and the LiDAR point cloud, established an error equation and added it to the bundle block adjustment for iterative calculation. They handled the problem of non-rigid distortion between the LiDAR point cloud and images, however, whether this method is suitable for large scenes such as aerial images still needs to be verified.…”

Section: Related Workmentioning

confidence: 99%

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

Zheng

Sun³

et al. 2019

Remote Sensing

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VLS (Vehicle-borne Laser Scanning) can easily scan the road surface in the close range with high density. UAV (Unmanned Aerial Vehicle) can capture a wider range of ground images. Due to the complementary features of platforms of VLS and UAV, combining the two methods becomes a more effective method of data acquisition. In this paper, a non-rigid method for the aerotriangulation of UAV images assisted by a vehicle-borne light detection and ranging (LiDAR) point cloud is proposed, which greatly reduces the number of control points and improves the automation. We convert the LiDAR point cloud-assisted aerotriangulation into a registration problem between two point clouds, which does not require complicated feature extraction and match between point cloud and images. Compared with the iterative closest point (ICP) algorithm, this method can address the non-rigid image distortion with a more rigorous adjustment model and a higher accuracy of aerotriangulation. The experimental results show that the constraint of the LiDAR point cloud ensures the high accuracy of the aerotriangulation, even in the absence of control points. The root-mean-square error (RMSE) of the checkpoints on the x, y, and z axes are 0.118 m, 0.163 m, and 0.084m, respectively, which verifies the reliability of the proposed method. As a necessary condition for joint mapping, the research based on VLS and UAV images in uncontrolled circumstances will greatly improve the efficiency of joint mapping and reduce its cost.

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“…Operations on line vectors are performed similar to those on Cartesian vector, also referred to as line dot-and cross-products [17], and are defined by:…”

Section: The Coordinate Transformation Of Line Vectorsmentioning

confidence: 99%

“…In registration based on linear features, Habib et al proposed to directly incorporate LiDAR lines representing a sequence of intermediate points along the line features as controls in bundle adjustment [16,17]. Liu et al proposed to establish coplanar equations of bundle adjustments by the coplanar constraints of linear features and collinear constraints of tie points [18].…”

Section: Introductionmentioning

confidence: 99%

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

et al. 2017

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Abstract:In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

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Registration of Optical Images with Lidar Data and Its Accuracy Assessment

Cited by 20 publications

References 18 publications

Color Restoration for Full-Waveform Multispectral LiDAR Data

Color Restoration for Full-Waveform Multispectral LiDAR Data

Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

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