Radiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets

Kaasalainen, Sanna; Hyyppä, Hannu; Kukko, Antero; Litkey, Paula; Ahokas, Eero; Hyyppä, Juha; Lehner, H.; Jaakkola, Anttoni; Suomalainen, Juha; Akujärvi, Anu; Kaasalainen, M.; Pyysalo, Ulla

doi:10.1109/tgrs.2008.2003351

Cited by 114 publications

(96 citation statements)

References 22 publications

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“…Consequently, the ability to accurately ratio wavelengths to derive vegetation indices is currently limited and was not evaluated. With ground reflectance target data (Kaasalainen et al 2009), however, this could be achieved and so is a logical next step in land cover or vegetation species classification.…”

Section: Discussionmentioning

confidence: 99%

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Hopkinson

Chasmer

Gynan³

et al. 2016

Canadian Journal of Remote Sensing

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Abstract. Airborne LiDAR is increasingly used in forest carbon, ecosystem, and resource monitoring. For practical design and manufacture reasons, the 1064 nm near-infrared (NIR) wavelength has been the most commonly adopted, and most literature in this field represents sampling characteristics in this wavelength. However, due to eye-safety and application-specific needs, other common wavelengths are 1550 nm and 532 nm. All provide canopy structure reconstructions that can be integrated or compared through space and time but the consistency or complementarity of 3D airborne LiDAR data sampled at multiple wavelengths is poorly understood. Here, we report on multispectral LiDAR missions carried out in 2013 and 2015 over a managed forest research site. The 1st used 3 independent sensors, and the 2nd used a single sensor carrying 3 lasers. The experiment revealed differences in proportions of returns at ground level, vertical foliage distributions, and gap probability across wavelengths. Canopy attenuation was greatest at 532 nm, presumably due to leaf tissue absorption. Relative to 1064 nm, foliage was undersampled at midheight percentiles at 1550 nm and 532 nm. Multisensor data demonstrated differences in foliage characterization due to combined influences of wavelength and acquisition configuration. Single-sensor multispectral data were more stable but demonstrated clear wavelength-dependent variation that could be exploited in intensity-based land cover classification without the aid of 3D derivatives. This work sets the stage for improvements in land surface classification and vertical foliage partitioning through the integration of active spectral and structural laser return information.

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Section: Discussionmentioning

confidence: 99%

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Hopkinson

Chasmer

Gynan³

et al. 2016

Canadian Journal of Remote Sensing

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“…Radiometric calibration procedures have been suggested and tested for both airborne and terrestrial laser scanning (Coren et al, 2006, Ahokas et al, 2006, Wagner et al, 2006, Höfle et al, 2007& Kaasalainen et al, 2009b. The intensity data is mostly applied in the processing and visualization of the range data, and most of the laser scanner studies are still being published without any radiometric calibration of the intensity data.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to link these parameters into laser scanner intensity. Some initial studies exist for TLS (Kaasalainen et al, 2011, Lichti et al, 2005, Pesci et al, 2008& Kukko et al, 2008 and ALS (Kaasalainen et al, 2009b& Lehner et al, 2010, but no correction methods have been established. Another thing that needs further study is the mixing of range and incidence angle effects in TLS data, because instrumental effects are known to affect the measured intensity for most TLS instruments, causing the radar equation not to be applicable at all ranges (Kaasalainen et al, 2011, Lichti et al, 2003, Kaasalainen et al, 2009a.…”

Section: Introductionmentioning

confidence: 99%

“…In many field applications (e.g., those where stationary TLS is carried out with the instrument mounted on a tripod) the incidence angle changes along with the increasing range. The incidence angle effect on ALS intensity has been assumed to be small in many studies (Kaasalainen et al, 2009b, Hollaus et al, 2010, but further studies are called for, especially because it has turned out that the incidence angle does have an effect on data quality and should be taken into account in normalizing ALS data from different flight paths (Jutzi et al, 2009). …”

Section: Introductionmentioning

confidence: 99%

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Correction of Intensity Incidence Angle Effect in Terrestrial Laser Scanning

Krooks

Kaasalainen

Hakala

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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KEY WORDS:Terrestrial laser scanning, Laser scanning intensity, incidence angle effect, ABSTRACT:In this article, we have studied the incidence angle effect on Terrestrial Laser Scanning (TLS) intensity. In previous tests, it has been found that the backscattered intensity of an object affects the incidence angle effect. We made additional experiments to investigate the potential mixing of distance and incidence angle effects and the role of surface parameters such as object grain size and scanning wavelength. The results indicate that distance and incidence angle effects do not mix and laboratory measured correction values can be used to correct intensity data from field-scanned point clouds. We also compared the laboratory measurements to real world surfaces to validate the correction procedures in practical TLS applications. The idea is also to make practical recommendations for TLS intensity correction in most common TLS applications.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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“…Various radiometric correction and calibration techniques have been developed for discrete-return or full-waveform LiDAR data based on the radar (range) equation (Höfle and Pfeifer, 2007;Kaasalainen et al, 2009;Wagner, 2010;Jutzi and Gross, 2010;Yan et al, 2012). The purpose of radiometric correction aims to retrieve the surface reflectance of the illuminated object for each of the received laser pulses.…”

Section: Radiometric Correctionmentioning

confidence: 99%

Reduction of Striping Noise in Overlapping Lidar Intensity Data by Radiometric Normalization

Yan¹,

Shaker²

2016

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

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ABSTRACT:To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech's Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (cv) of the intensity values before and after implementing the proposed method. Reduction of cv was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.

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Radiometric Calibration of LIDAR Intensity With Commercially Available Reference Targets

Cited by 114 publications

References 22 publications

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Correction of Intensity Incidence Angle Effect in Terrestrial Laser Scanning

Reduction of Striping Noise in Overlapping Lidar Intensity Data by Radiometric Normalization

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