Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

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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“…Using external calibration tiepoints, the intensity can also be used in multi-temporal remote sensing. (Höfle et al, 2007& Yan et al, 2012.…”

Section: Introductionmentioning

confidence: 99%

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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“…Regardless of discrete-return or full-waveform LiDAR data, such intensity noise is mainly caused by the signal attenuation due to various system and environmental factors (Jelalian, 1992). Though various correction and calibration techniques have been proposed to reduce the intensity discrepancy based on the use of radar (range) equation (Höfle and Pfeifer, 2007;Wagner, 2010;Yan et al, 2012), only a few studies address the striping noise issue when dealing with the overlapping LiDAR data strips. Luzum et al (2004) proposed a method to normalize the observed LiDAR intensity by multiplying a dynamic range factor to the power of f (f = 2), where such dynamic range factor equals to the range of the observed point divided by a standard range.…”

Section: Introductionmentioning

confidence: 99%

“…range, scan angle, atmospheric attenuation coefficients, etc.) are available, radiometric correction can be applied to the original intensity (OI) data (Yan et al, 2012;Yan and Shaker, 2014). Conceptually, the spectral reflectance (or radiometrically corrected intensity (RCI)) is determined for each of the LiDAR data strips (X A and X B ) after radiometric correction.…”

Section: Introductionmentioning

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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“…The quantitative use of these images requires the geometric distortions in them to be corrected, or rectified, to a desired map projection. Geometric correction, also known as georeferencing and image rectification, is necessary when the output products of image analysis are overlayed on a map or merged into a geographic database [Fontinovo et al, 2012;Gianinetto, 2012;Yan et al, 2012;Wang et al, 2012;Arun and Katiyar, 2013]. Sertel et al [2007] and Topan and Kutoglu [2009] introduced the figure condition method to analyze the accuracy of geometric correction.…”

Section: Introductionmentioning

confidence: 99%

Optimal Ground Control Points for Geometric Correction Using Genetic Algorithm with Global Accuracy

Nguyen

2015

European Journal of Remote Sensing

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Establishing the ground control point (GCP) network is a pre-requisite for georeferencing raw image data. Given current typical digital spatial database quality, much interest among users is about the accuracy of the geometric correction model that yields the final product. This paper reports an approach to optimizing GCP assembly using a genetic/evolution algorithm. The paper also suggests an optimal criterion for accuracy assessment through appraisal of global accuracy of the transformation, which is computed at each point of the image space. Experimental results demonstrate that the proposed approach has a great potential for selection of the best GCPs, and considerable improvement to the accuracy of geometric correction models can be expected when it is implemented.

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Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction

Cited by 99 publications

References 27 publications

Correction of Intensity Incidence Angle Effect in Terrestrial Laser Scanning

Correction of Intensity Incidence Angle Effect in Terrestrial Laser Scanning

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

Optimal Ground Control Points for Geometric Correction Using Genetic Algorithm with Global Accuracy

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