Radiometric correction of laser scanning intensity data applied for terrestrial laser scanning

Sanchiz-Viel, Nathan; Bretagne, Estelle; Mouaddib, El Mustapha; Dassonvalle, P.

doi:10.1016/j.isprsjprs.2020.11.015

Cited by 13 publications

(12 citation statements)

References 47 publications

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“…Intensity correction methods [30] make the intensity surface-independent and eliminate the effects of both range and incidence angle. Several studies [30,31] have used radar equations to derive the backscatter equation, aiming to achieve intensity correction. The model-driven method uses a target surface that is dependent on the model assumptions of the backscattering coefficient.…”

Section: Observations Of Intensity Measuresmentioning

confidence: 99%

“…However, actual data are inconsistent with the theoretical model, and researchers have found that instrumental factors dominate the range effect [32]. Many recent studies have focused on empirical/datadriven calibration processes for terrestrial laser scanner (TLS) intensity [30,33,34].…”

Section: Observations Of Intensity Measuresmentioning

confidence: 99%

“…Bretagner et al [33] proposed a data-driven calibration process for the TLS intensity. The intensity of the spherical reference target for a range r and incidence angle α was approximated using a surface-fitting approach [30,33], as follows:…”

Section: Observations Of Intensity Measuresmentioning

confidence: 99%

“…There is no unified form for describing the parameters that influence the intensity of most outdoor roads. Some conclusions can be obtained from [30,33] and the theoretical radar equations-the returned intensity is proportional to the cosine of the angle of the beam to the surface normal, the intensity value is proportional to the inverse of the range value, and the predominant parameters are the range value and cosine of the incidence angle. However, according to the spatial analyses using equations ( 2) and ( 3), the cosine of the incidence angle is the primary factor, and the range value depends on it.…”

Section: Intensity Feature Extraction From One Laser Scanmentioning

confidence: 99%

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Road classification using 3D LiDAR sensor on vehicle

Liu

Zhao

Zhang

2023

Meas. Sci. Technol.

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Road classification is essential in outdoor vehicle perception systems to evaluate driving scenarios. A light detection and ranging (LiDAR) sensor can provide accurate measurements of an environment composed of range, intensity, and angular information. This study proposes a weakly supervised learning algorithm for classifying road surface types based on laser measurements. The proposed algorithm first separates the laser points on the road surface based on the angular structure of the initial range measures obtained using 3D LiDAR in a noniterative manner. Then, a physical-data-driven model that represents the retroreflective nature of the different surface types is abstracted from the intensity measures and their factors. The linear regression parameters of the model are employed as feature descriptions of the road surface. The K-means method is used to classify the different road types. The learning process requires weak supervision at the training centroid stage and is robust to new road types. The classification performance was evaluated in different outdoor scenarios, and the classification accuracies exceeded 98%. The proposed method outperforms similar existing methods, performs at the same frequency as that of LiDAR, and can be implemented in real time.

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Section: Observations Of Intensity Measuresmentioning

confidence: 99%

Section: Observations Of Intensity Measuresmentioning

confidence: 99%

Section: Observations Of Intensity Measuresmentioning

confidence: 99%

Section: Intensity Feature Extraction From One Laser Scanmentioning

confidence: 99%

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Road classification using 3D LiDAR sensor on vehicle

Liu

Zhao

Zhang

2023

Meas. Sci. Technol.

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“…Instrument specification, plane residual accuracy, required point spacing, target color, and target gloss are also factors which users should consider when selecting scanning positions [ 92 ]. Moreover, two methods were applied for radiometric correction of laser scanning intensity data [ 93 ]. It should be noted that there is still a lack of standardized tests for quantifying the impact of various error sources, as well as uniform accuracy requirements.…”

Section: Key Issues In the Application Of Tls Technologymentioning

confidence: 99%

Review on the Research and Applications of TLS in Ground Surface and Constructions Deformation Monitoring

Teng

Shi

Wang

et al. 2022

Sensors

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With the gradual maturity of the terrestrial laser scanners (TLS) technology, it is widely used in the field of deformation monitoring due to its fast, automated, and non-contact data acquisition capabilities. The TLS technology has changed the traditional deformation monitoring mode which relies on single-point monitoring. This paper analyzes the application of TLS in deformation monitoring, especially in the field of ground surface, dam, tunnel, and tall constructions. We divide the methods for obtaining ground surface deformation into two categories: the method based on point cloud distance and the method based on displacement field. The advantages and disadvantages of the four methods (M2M, C2C, C2M, M3C2) based on point cloud distance are analyzed and summarized. The deformation monitoring methods and precisions based on TLS for dams, tunnels, and tall constructions are summarized, as well as the various focuses of different monitoring objects. Additionally, their limitations and development directions in the corresponding fields are analyzed. The error sources of TLS point cloud data and error correction models are discussed. Finally, the limitations and future research directions of TLS in the field of deformation monitoring are presented in detail.

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