Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data

Errington, Angus F. C.; Daku, B.L.F.

doi:10.3390/rs9040356

Cited by 16 publications

(13 citation statements)

References 20 publications

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“…A physical modelling of such an effective footprint is not straightforward, even if some physical bases were provided in Reference [11]. However, the physical approach is limited with scanner such as the FARO, as several factors affect the registered value of phase-shift scanner intensity, such as temperature [31] and shooting direction in the different phases. The methodology presented here provided a promising alternative to calibrate the sensitivity to distance.…”

Section: The Sensitivity To Distance To Scanner Led To the Notion Of mentioning

confidence: 99%

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

et al. 2018

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Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods.

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Section: The Sensitivity To Distance To Scanner Led To the Notion Of mentioning

confidence: 99%

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

et al. 2018

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“…Since the existing methods are unable to estimate the distance-intensity relationship for long-distance TLSs, a comparison between the improved and existing methods are not conducted in this study. The instrumental configurations (e.g., instrument temperature [16]) and atmospheric conditions (e.g., humidity, pressure) are ignored in the study. Considering these effects would further improve the intensity correction accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Since almost no points could be extracted at incidence angles larger than 85 • , we did not measure the intensity data for the four targets at incidence angles from 85 • to 90 • . In contrast to the distance effect that was very irregular, it had been proven that the overall trend of intensity with respect to incidence angle was relatively regular [10,16,18,[18][19][20]. The intensity decreased with an increase of the incidence angle from 0 • to 90 • .…”

Section: Indoor Experimentsmentioning

confidence: 98%

“…The four targets have good diffuse reflection characteristics and can be approximately considered as Lambertian. Lambertian targets have good homogeneity and have been adopted to derive the incidence angle-intensity relationship in many studies [10,16,18,19,25,30]. As only a relative intensity correction was conducted in this study, there was no need to know the specific reflectance values.…”

Section: Indoor Experimentsmentioning

confidence: 99%

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Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone

et al. 2019

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The intensity data recorded by a terrestrial laser scanner (TLS) contain spectral characteristics of a scanned target and are mainly influenced by incidence angle and distance. In this study, an improved implementable method is proposed to empirically correct the intensity data of long-distance TLSs. Similar to existing methods, the incidence angle–intensity relationship is estimated using some reference targets scanned in the laboratory. By contrast, due to the length limit of indoor environments and the laborious data processing, the distance–intensity relationship is derived by selecting some natural homogeneous targets with distances covering the entire distance scale of the adopted long-distance TLS. A case study of intensity correction and point cloud classification in an intertidal zone in Chongming Island, Shanghai, China, is conducted to validate the feasibility of the improved method by using the intensity data of a long-distance TLS (Riegl VZ-4000). Results indicate that the improved method can accurately eliminate the effects of incidence angle and distance on the intensity data of long-distance TLSs; the coefficient of variation of the intensity data for the targets in the study intertidal zone can be reduced by approximately 54%. The classification results of the study intertidal zone show that the improved method can effectively eliminate the variations caused by the incidence angle and distance in the original intensity data of the same target to obtain a corrected intensity that merely depends on target characteristics for improving classification accuracy by 49%.

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“…* Corresponding author With various radiometric pre-processing techniques being proposed and developed (Kashani et al, 2015), LiDAR data users reap the benefits of using the monochromatic laser intensity data for surface classification and object recognition (Yan et al, 2015). Most of the existing radiometric calibration and correction models are built upon the radar (range) equation that considers the system-and environment induced distortions, including the range (Kaasalainen et al, 2011), incidence angle (You et al, 2017), atmospheric attenuation (Errington and Daku, 2017) and other system parameters (Vain et al, 2010). To minimize the discrepancy in overlapping strips, certain radiometric normalization approaches have been proposed Shaker, 2014, 2016) which have proven to reduce the striping noise found within the overlapping region.…”

Section: Introductionmentioning

confidence: 99%

Correction of Overlapping Multispectral Lidar Intensity Data: Polynomial Approximation of Range and Angle Effects

Yan

Shaker

2017

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

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ABSTRACT:Recent development of radiometric calibration, correction and normalization approaches have facilitated the use of monochromatic LiDAR intensity and waveform data for land surface analysis and classification. Despite the recent successful attempts, the majority of existing approaches are mainly tailor made for monochromatic LiDAR toward specific land surface scenario. In view of the latest development of multispectral LiDAR sensor, such as the Optech Titan manufactured by Teledyne Optech, a more generic approach should be developed so that the radiometric correction model is able to handle and compensate the laser energy loss with respect to different wavelengths. In this study, we propose a semi-physical approach that aims to utilize high order polynomial functions to model the distortion effects due to the range and the angle. To estimate the parameters of the respect polynomial functions for the range and angle, our approach first locates a pair of closest points within the overlapping LiDAR data strips and subsequently uses a non-linear least squares adjustment to retrieve the polynomial parameters based on the Levenberg-Marquardt algorithm. The approach was tested on a multispectral airborne LiDAR dataset collected by the Optech Titan for the Petawawa Research Forest located in Ontario, Canada. The experimental results demonstrated that the coefficient of variation of the intensity of channel 1 (1550 nm), channel 2 (1064 nm) and channel 3 (532 nm) were reduced by 0.1% to 39%, 10% to 45% and 12% to 54%, respectively. The striping noises, no matter found within single strip and overlapping strips, were significantly reduced after implementing the proposed radiometric correction.

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Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data

Cited by 16 publications

References 20 publications

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties

Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone

Correction of Overlapping Multispectral Lidar Intensity Data: Polynomial Approximation of Range and Angle Effects

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