ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44

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ISPRS Journal of Photogrammetry and Remote Sensing

journal homepage: www.elsevier .com/ locate/ isprs jprs

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

Wai Yeung Yan a, Ahmed Shaker a,⇑, Ayman Habib b, Ana Paula Kersting b

a Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Ont., Canada M5B 2K3b Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4

a r t i c l e i n f o

Article history:Received 21 April 2011Received in revised form 11 October 2011Accepted 12 October 2011Available online 11 November 2011

Keywords:LiDARGeometric calibrationRadiometric correctionIntensity dataClassification

0924-2716/$ - see front matter � 2011 Internationaldoi:10.1016/j.isprsjprs.2011.10.005

⇑ Corresponding author. Tel.: +1 416 979 5000x645E-mail address: ahmed.shaker@ryerson.ca (A. Shak

a b s t r a c t

Airborne light detection and ranging (LiDAR) systems are used to measure the range (distance from thesensor to the target) and the intensity data (the backscattered energy from the target). LiDAR has beenused extensively to model the topography of the Earth surface. Nowadays, LiDAR systems operating inthe near-infrared spectral range are also gaining high interest for land cover classification and object rec-ognition. LiDAR system requires geometric calibration (GC) and radiometric correction (RC) in order tomaximize the benefit from the collected LiDAR data. This paper evaluates the impact of the GC and theRC of the LiDAR data on land cover classification. The procedure includes the use of a quasi-rigorousmethod for the GC and the radar (range) equation for the RC of the LiDAR data. The geometric calibrationprocedure is used to adjust the coordinates of the point cloud by removing the impact of biases in thesystem parameters as well as deriving corrected ranges and scan angles (in the absence of the system’sraw measurements) for the RC process. The geometrically calibrated ranges and scan angles are then usedto correct the intensity data from the atmospheric attenuation and background backscattering based onthe radar (range) equation. The atmospheric attenuation, which has not been fully addressed in the pre-vious literature, is modeled by considering the parameters of absorption as well as scattering derivedfrom existing empirical models and public (free) molecular absorption database. A LiDAR dataset cover-ing an urban area is used to evaluate the effect of the GC and RC of the LiDAR data on land cover classi-fication. The results are evaluated using a true ortho-rectified aerial image acquired during the sameflight mission. The classification results show an accuracy improvement of about 9.4–12.8% for the LiDARdata used after the GC and RC. The study provides a practical approach for the LiDAR system GC and RCwhich can be implemented by most of the data end users. The outcome from this research work is a dataprocessing tool that maximizes the benefits of using the intensity data for object recognition and landcover classification, which will be quite important for LiDAR data users.� 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier

B.V. All rights reserved.

1. Introduction

Airborne light detection and ranging (LiDAR) systems aremainly used to obtain the 3D coordinates of objects on the Earthsurface for the generation of digital elevation/surface models(DEM/DSM). Feature extraction and object recognition/reconstruc-tion using airborne LiDAR data mainly rely on the geometry of the3D point clouds (Zhang et al., 2006; Dorninger and Pfeifer, 2008).Commercial LiDAR sensors usually utilize Nd:YAG laser whichhas a wavelength of 1.064 lm. High separability of spectral reflec-tance can always be found amongst different materials in thisnear-infrared spectral range. In this regard, the peak backscatteredlaser energy from different objects (intensity data) can be utilized

Society for Photogrammetry and R

8; fax: +1 416 979 5122.er).

to distinguish different land cover features. A number of studiesinvestigated the use of LiDAR intensity data, which was fused withother ancillary data for different applications (Bork and Su, 2007;Dalponte et al., 2008). The use of LiDAR intensity data for land cov-er classification and object recognition has been also explored.

Song et al. (2002) interpolated the intensity data of the pointcloud into grid data and applied image filters to remove noisewithin the intensity data. The separability amongst four land coverclasses (grass, house, road and tree) was assessed and low separa-bility was found between grass and tree classes. To enhance thequality of the intensity data classification, similar studies havebeen conducted by incorporating ancillary data. Beasy et al.(2005) classified near shore materials (bedrock, cobble, and sand)at the Fundy coast of Nova Scotia using intensity data, texture data,and the luminance data (the average digital numbers of ortho-rec-tified aerial image). Fairly high separability was found in the

emote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

36 W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44

experiment amongst the classes with average Bhattacharrya dis-tance near to 1.9. Goodale et al. (2007) utilized LiDAR intensityand elevation data to classify coastal estuaries and beach habitat(such as mudflat, sand, cobble, tree and shrub) with a logical filterclassification model. It was found that LiDAR intensity data wasuseful in distinguishing coastal features. Object-oriented imagesegmentation has been proposed for land cover classification usingLiDAR range and intensity data. Overall accuracy of about 90% wasreported by Brennan and Webster (2006), and Antonarakis et al.(2008). Despite that LiDAR data has demonstrated its usefulnessin several applications, geometric calibration (GC) and radiometriccorrection (RC) should be carried out in order to fully explore itspositional and reflectance (intensity) information.

Biases in LiDAR system parameters/measurements will lead tosystematic errors in the derived point cloud coordinates. Existingapproaches for reducing/eliminating the impact of systematic er-rors can be categorized into system driven (calibration) and datadriven (strip adjustment) methods. System driven (or calibration)methods are based on the physical sensor model relating the sys-tem measurements/parameters to the ground coordinates of theLiDAR points. These methods incorporate the system’s raw data(e.g., Filin, 2001; Skaloud and Lichti, 2006; Friess, 2006) or at leastthe trajectory and time-tagged point cloud (Burman, 2000; Toth,2002; Morin, 2002) for the estimation of biases in the systemparameters/measurements with the help of the LiDAR equation.The access to the system raw measurements is usually restrictedto LiDAR system manufacturers. Therefore, data driven (stripadjustment) methods using the XYZ coordinates of the LiDAR pointcloud are proposed in the absence of the raw measurements (Kilianet al., 1996; Crombaghs et al., 2000; Maas, 2002; Filin and Vossel-man, 2004). The major drawback of such methods is the relianceon arbitrary coordinate transformation model between the laserstrip coordinate system and the reference data coordinate system.The utilized transformation function might not be appropriate andwill change depending on the nature of the inherent biases in theLiDAR system parameters.

Recently, Habib et al. (2010) proposed a geometric (system) cal-ibration approach denoted as ‘‘Quasi-Rigorous’’. This approach ismore flexible when compared to its predecessor, the ‘‘SimplifiedCalibration’’ – which has been proposed in the same paper, interms of the required flight configuration; i.e., it can be used fordatasets consisting of non-parallel flight lines and has no restric-tion in terms of the terrain characteristics. To use the quasi-rigor-ous calibration approach, besides the LiDAR point cloudcoordinates, the user should also have access to the trajectory po-sition as well as the time-tagged point cloud. Since this calibrationprocedure derives approximations of some of the system raw mea-surements, it can provide as a by-product the necessary informa-tion for the RC of the LiDAR intensity data (i.e., improved scanmirror angles and ranges). For this reason, this method will be usedin this paper.

The radiometric correction of the LiDAR data aims at removingthe effects of laser energy attenuation due to the atmospheric ef-fects and the object surface backscattering. Radiometric correctionof the LiDAR data can be performed using empirical or physical ap-proaches (Höfle and Pfeifer, 2007). The empirical approach doesnot consider the physical properties of the laser backscattering en-ergy. Instead, it introduces statistical analysis to minimize thenoise in the intensity data. Boyd and Hill (2007) attempted to val-idate the intensity data with HyMap sensor data (Band 42), whichhas a similar spectral wavelength, over forested area. Correlationcan be found in a few forest species between the two datasetsbut a practical RC method is still required. Höfle and Pfeifer(2007) introduced an empirical approach by deriving a polynomialmodel based on the range and intensity data. A least squaresadjustment was conducted to fit the model by selecting

homogenous backscattered areas in the LiDAR dataset. The resultsachieved showed significant reduction of the intensity variation inthe homogeneous areas.

On the other hand, physical approach relies on the use of radar(range) equation (Jelalian, 1992), and it was adopted for RC of Li-DAR intensity data by Coren and Sterzai (2006), Höfle and Pfeifer(2007), and Kaasalainen et al. (2007). The studies were conductedto calibrate the LiDAR intensity data acquired from a number ofcommercial LiDAR sensors using artificial targets (Kaasalainenet al., 2009a) and natural targets (Vain et al., 2009). The effects ofthe flying height (Vain et al., 2009), range (Kaasalainen et al.,2009b), incidence angle (Kukko et al., 2008), sensor aperture size(Kaasalainen and Kaasalainen, 2008), surface moisture (Kaasalai-nen et al., 2010), automatic gain control on the backscatteredintensity (Vain et al., 2010), and reflection model (Jutzi and Gross,2010) have been studied. The research results contributed to acomprehensive solution for radiometric calibration and correctionby investigating the effects of different systems and environmentalparameters. However, some of the factors, such as the atmosphericabsorption and scattering, have not been fully investigated and apractical approach to integrate all these factors without field/labo-ratory measurements is still needed.

The above review indicates that GC and RC of LiDAR data havebeen addressed in the previous research work. Nevertheless, veryfew authors have investigated the impact of the GC and RC of theLiDAR data on surface classification and object recognition. Thispaper demonstrates practical approaches for both GC and RC of air-borne LiDAR data. The paper addresses the impact of the GC and RCon the accuracy of land cover classification. The quasi-rigorousmethod is introduced for GC of the range and the scan mirror an-gle; then the RC of the intensity data is developed based on the ra-dar (range) equation. Experimental results using real LiDAR dataare presented to evaluate their impact on the quality of the LiDARpoint cloud and on the land cover classification. Finally, the paperpresents some conclusions and recommendations for future work.

2. Methodology

2.1. Overall workflow

Fig. 1 shows the workflow of the GC and RC of the intensity dataand the assessment of the land cover classification. The geometriccalibration is conducted by the ‘‘Quasi-Rigorous’’ method pre-sented in Habib et al. (2010). The conceptual basis of the ‘‘Quasi-Rigorous’’ calibration procedure and the derivation of the neces-sary parameters for the radiometric correction process are outlinedin Section 2.2. The geometrically calibrated LiDAR data (the rangeand the scan angle) together with the original intensity data (I)are then used for the RC based on the radar (range) equation.Weather data are imported for the calculation of the atmosphericattenuation of the laser beam. Then, RC intensity data is calculatedas it is explained in the following section. Intensity images are cre-ated using: (i) the original intensity data, (ii) the GC intensity data,(iii) the RC intensity data, and (iv) the GC and RC intensity data. Aparametric classification decision rule, i.e., the Gaussian maximumLikelihood, is used to classify the intensity image data and compar-isons amongst the classification results of the four LiDAR datasetsare conducted. The results of the classification are assessed basedon the overall accuracy and the percentage of accuracy improve-ment is evaluated.

2.2. Geometric calibration (GC)

The ‘‘Quasi-Rigorous’’ procedure, which has been proposed byHabib et al. (2010) is implemented for the GC of the point cloud.

Radiometric Correction

Weather Information

HITRAN Database

Atmospheric Attenuation

Orthoimage

Accuracy Assessment Land Cover Map

Gaussian Maximum Likelihood Classification

Original LiDAR Intensity Data

Geometric Calibration

GC Intensity

GC& RC Intensity RC Intensity

Fig. 1. Experimental workflow for the geometric calibration and radiometric correction of LiDAR data and land cover classification.

Fig. 2. Illustration of the reflected angle hr for a laser beam.

W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44 37

This procedure requires time-tagged point cloud and trajectory po-sition data. It utilizes the LiDAR data in overlapping strips togetherwith control points for estimating the biases in the system param-eters by reducing the discrepancies between conjugate surface ele-ments in overlapping strips and the control surface. The estimatedbiases in the system parameters are then used to reconstruct ad-justed LiDAR point cloud coordinates in the different strips andan iterative process is conducted to make a better estimate of thesystem biases. After convergence, the adjusted coordinates areused to evaluate the corrected ranges (r) and scan mirror angles(b) as follows:

(i) For a LiDAR point mapped at time t, trajectory positionswithin a certain time interval (t � Dt, t + Dt) are identified.

(ii) Then, a straight line is fitted through the selected trajectorypositions to come up with a local estimate of the trajectory.

(iii) After that, x, which is the x-coordinate of the laser point withrespect to the laser unit coordinate system, i.e., the lateraldistance with the appropriate sign between the adjustedLiDAR point in question and the projection of the flight tra-jectory onto the ground, can be determined.

(iv) Finally, b and r can be computed by simple trigonometricoperations using the estimated lateral distance x and the tra-jectory height above ground.

For more details regarding the implementation of the ‘‘Quasi-Rigorous’’ calibration procedure, interested readers can refer toHabib et al. (2010).

2.3. Radiometric correction (RC)

2.3.1. Radar (range) equationThe effects on the physical properties of the received laser beam

energy (Pr) are considered using the radar (range) equation pre-sented in Eq. (1), which takes into account the sensor configurationand different environmental parameters. The reflected laser beamenergy (Pr) depends on two groups of parameters: (a) systemparameters, and (b) environmental parameters. The system param-eters refer to the configuration and the characteristics of the laserscanning system including the transmitted laser energy PT, the gainfactor of the antenna GT, the aperture diameter D, the range of each

laser pulse r, and the loss due to system inefficiency gsys. Some ofthe factors such as PT, D, gsys are assumed to be constant duringthe flight (Höfle and Pfeifer, 2007). The environmental parametersinclude the laser target cross section r and the atmospheric atten-uation gatm

Pr ¼PT GT

4pr2

r4pr2

pD2

4gsysgatm ð1Þ

The target cross section presented in Eq. (2) is one of the keyparameters to be considered in the radar (range) equation. It de-pends on the characteristics of the target surface (slope and aspect)with respect to the direction of the laser pulse. The qs in Eq. (2) re-fers to the spectral reflectance at the specific wavelength and Atarget

refers to the target area which is illuminated by the footprint of thelaser beam. The scattering from the target area is usually not uni-form because almost all surfaces by nature are rough. The Lamber-tian assumption is adopted to model the surface reflectance.Following this assumption, the scattered laser pulse is constantat all reflected angles and the irradiance from the target is propor-tional to the cosine of the reflected angle hr (Steinvall, 2000). The hr

(the angle between the incidence laser beam and the surface nor-mal of the ground object, see Fig. 2) can be assumed equal to thescan angle (b) in case where the ground surface is relatively flat.However, this is not always the case particularly in urban areasand rugged terrain. A better estimate of the angle hr can be calcu-lated using the b and surface slope as defined by the LiDAR datapoint cloud. With the assumption that the intensity represents

38 W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44

the peak value of Pr (Höfle and Pfeifer, 2007), data providers line-arly transformed Pr into 8 bit values to represent the intensity datawhich is used in the radar (range) equation (Höfle and Pfeifer,2007)

r ¼ 4pqSAtarget cosðhrÞ ð2Þ

2.3.2. Atmospheric attenuationAtmospheric attenuation measured by the parameter gatm is

one of the major factors affecting the radar (range) equation (Jenn,2005). The gatm follows the Beer-Lambert Law where the laser en-ergy is attenuated in an exponential manner as presented below:

gatm ¼ e�2ar ð3Þ

where

a ¼ sasðkÞ þ smsðkÞ þ saaðkÞ þ smaðkÞ ð4Þ

and a refers to the power of the extinction coefficients which iswavelength (k) dependent, and the attenuation varies spatiallyand temporally (Hayes and Latham, 1975). The extinction coeffi-cient a is the summation of: (a) the aerosol scattering (sas), (b)the molecular (Rayleigh) scattering (sms), (c) the aerosol absorption(saa), and (d) the molecular absorption (sma). A number of studieshave been conducted to model the atmospheric extinction for thetroposphere. As the atmospheric extinction of Nd:YAG laser is notfully investigated, the effects of scattering and absorption are mod-eled using the following formulas and empirical models.

2.3.3. Aerosol scatteringThe aerosol scattering (or Mie scattering) is mainly due to the

short wavelength scattering caused by small particles suspendedin the air such as dust, smoke or droplets of salt water. The aerosolscattering is difficult to model due to the lack of instantaneous aer-osol measurements including the composition, concentration, anddistribution in the air. Therefore, empirical approach is developedto model the aerosol extinction in the atmosphere based on thewavelength and visibility. The model developed by Filippov et al.(1982) is commonly used where aerosol extinction is formulatedas sas ¼ 3:91ðn0 þ n1k

�n2 Þ=v where v is the meteorological visibilityrange measured in km, and n0, n1, and n2 are fitting coefficients. Inthis study, the revised model proposed by Ferdinandov et al. (2009)is used to characterize the aerosol scattering for near Earth surfaceas shown in Eq. (5)

sas ¼ ð�2:656 lnðkÞ þ 2:449Þ � v�0:199 lnðkÞþ1:157 ð5Þ

where k is the wavelength in lm and v is the meteorological visibil-ity range measured in km. This model is claimed to be suitable forclose to ground troposphere. In addition, the model was evaluatedusing different data acquired from the previous research publishedfrom 1957 to 2008.

2.3.4. Rayleigh scatteringThe Rayleigh scattering is caused by small air particles and clus-

ters in the atmosphere. The scattering is significant for electromag-netic radiation with short wavelength. Bucholtz (1995) introduceda set of formulas for the Rayleigh scattering cross-section calcula-tion with reference to one standard atmospheric model (the 1962US standard) and five supplementary models (tropical, mid-lati-tude summer, mid-latitude winter, subarctic summer, and subarc-tic winter). To calculate the extinction coefficients, the totalRayleigh scattering cross section per molecule rr for a wavelength(k) is given by:

rrðkÞ ¼24p3ðnsðkÞ2 � 1Þ2

k4N2S ðnSðkÞ2 þ 2Þ2

Fk ð6Þ

where NS is the molecular density (2.54743 � 1019 cm�3) for stan-dard air, the Fk is the King correction factor and Fk = (6 + 3qn)/(6–7qn), and the qn is the depolarization factor which accounts forthe anisotropy of the air molecule. Although Fk is not provided forwavelength greater than 1 lm in Bucholtz (1995), the correspond-ing value of Fk in 1.064 lm can be retrieved from the recent recal-culation of Rayleigh scattering in Tomasi et al. (2005). The termns(k) is the refractive index for standard air for a specific wave-length (k). It can be calculated using Eq. (7) for wavelengths greaterthan 0.23 lm:

ðnSðkÞ � 1Þ � 108 ¼ 5;791;817

238:0185� ð1=kÞ2þ 167;909

57:362� ð1=kÞ2ð7Þ

The total Rayleigh scattering coefficient sms for a specific wave-length (k) is the product of the total Rayleigh cross section per mol-ecule rr(k) calculated from Eq. (6) and the molecular density N at agiven pressure and temperature (i.e., sms(k) = N rr(k)). As N is prac-tically difficult to be measured for most of the applications, the to-tal Rayleigh volume-scattering coefficient sms(k) can be derived bynormalizing the pressure (P) and temperature (T) with respect tothe corresponding values at standard air NS (Eq. (8)). The atmo-spheric model for the standard air used in this study is the mid-lat-itude summer model with standard pressure (Ps) = 1013 mbars andstandard temperature (Ts) = 294 K

smsðkÞ ¼ NSrrðkÞPPS

TS

Tð8Þ

2.3.5. Aerosol and molecular absorptionsAerosol and molecular absorption may cause energy loss of the

laser beam through propagation due to the existence of the watervapour, carbon dioxide, oxygen, etc. (Zuev, 1976). The absorptionattenuates the energy of the laser pulse when it travels from thesensor to the ground and vice versa. Referring to the atmospherictransmission windows, the major contributor to the absorptionat wavelength 1.064 lm is the water vapor. To find the extinctioncoefficient, public (free) molecular absorption database such as HI-TRAN 2008 database (Rothman et al., 2009) can be used. The HI-TRAN database contains 2.7 million spectral lines for 42 differentmolecules. By selecting the specific molecules, range of spectrallines, temperature and pressure, the absorption coefficient can beretrieved from the database. In this study, the absorption coeffi-cient of water vapor at the operation wavelength (1.064 lm) ofthe LiDAR sensor is retrieved from the database.

The process described in this section is used to calculate thespectral reflectance qs which is converted and presented as theRC intensity data. The LiDAR intensity data is then interpolatedinto digital image in ESRI ArcGIS for image classification. The inter-polation process is applied to the original intensity data and the GCand RC intensity data. Then, Gaussian maximum likelihood classi-fication is conducted on the interpolated intensity data and theclassification results are assessed using ground truth derived froma true ortho-rectified aerial image (Habib et al., 2007).

3. Study area and dataset

The study area covers the British Columbia Institute of Technol-ogy (BCIT) located at the Burnaby, British Columbia, Canada(122�590W, 49�150N). A LiDAR dataset was acquired to test the fea-sibility of the proposed GC and RC methods. The LiDAR missionwas conducted on July 17, 2009 at local time 14:55. The surveyingday was a sunny day with a temperature of 29.8 �C. The verticalvisibility and the pressure at that date and time were 48.3 kmand 101.81 kPa, respectively, as delivered by the National ClimateData and Information Archive from Environment Canada.

W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44 39

The LiDAR sensor used was Leica ALS50 operating in 1.064 lmwavelength and 0.33 mrad beam divergence. The average flyingheight is 600 m which leads to a point density of 4–5 points permeter square. The acquired data consists of a 3D point cloud withmultiple returns (up to 4 returns at maximum) in LAS format to-gether with the trajectory data. The LAS data file contains the xyzcoordinates, linearized intensity value in 8 bit, the number of thegiven return, the total number of returns, and the time of eachpulse of the point cloud. The trajectory data contains the xyz coor-dinates of the sensor and the time when the data was capturedduring the survey. The dataset also contains a geometrically cor-rected orthophoto, produced using images captured at the sametime of the LiDAR scanning mission, for the accuracy assessmentof land cover classification. The ortho-rectified aerial image con-sists of three bands (Red, Green, and Blue) with 0.5 m spatialresolution.

A subset of a single LiDAR strip is clipped with the dimension of500 m � 400 m for the experimental testing. The direction of theflight survey for this subset is from west to east (see Fig. 3). Thereason of selecting this particular area of the BCIT campus ismainly due to the variety of the land cover features on the ground.The area contains buildings, parking lots connected by sidewalksand pavements, shrubs and open spaces with grass coverage. Thesouth-west side of the study area has higher elevation than thenorth-east areas by 10 m. Dense tree clusters are present in westside of the study area. The subset of the LiDAR data has about 1million points and they are geometrically calibrated using the qua-si-rigorous method described previously. As discussed in Sec-tion 2.1, four datasets (original intensity data, GC intensity data,RC intensity data, and the GC and RC intensity data) are used forland cover classification and comparative analysis.

4. Results and discussion

4.1. Effects of the geometric calibration and radiometric correction onspectral separability of different classes

The results from the GC are reported in Habib et al. (2010). Ithas been noted that a significant bias was found in the boresightroll angle followed by a bias in the boresight pitch angle. Theimprovement in the strips compatibility before and after the cali-bration procedure is illustrated in Habib et al. (2010). The qualita-tive assessment has demonstrated a significant improvement invisual appearance of the generated intensity images from multiplestrips as well as the quality of fit between overlapping strips

Fig. 3. Study area in British Columbia Institut

(visually checked in drawn profiles) before and after the calibrationprocedure. The GC LiDAR data is then used for the process of theradiometric correction before the land cover classification.

By applying the radar (range) equation presented in Eqs. (1) and(2), the spectral reflectance qs, denoted as RC intensity data, iscomputed from the original intensity data. Then, both the originaland the RC intensity data are linearized into 8 bit radiometric res-olution and interpolated into an intensity image data. Fig. 4 shows3D representation of the interpolated intensity image data of theentire study area from the original and the RC intensity data. Visualinspection of the intensity data before and after the RC shows thatthe variations of the intensity values within homogenous areas arereduced after the RC. The peak intensity values (red color in Fig. 5)are usually located in the main roads, building roofs, and parkinglots. This can be explained due to the existence of vehicles on theroads and different equipments on roofs which have high reflec-tance in the near-infrared range (Berdahl and Bretz, 1997). Theoccurrence and the values of the peak intensity are also found tobe reduced after the RC.

To further investigate the effects of the RC on the intensity data,the methodology presented by Höfle and Pfeifer (2007) is followedto compare the differences between the original intensity valuesand the RC intensity values. Fig. 5 shows the differences of theintensity values before and after the RC where the original inten-sity data is subtracted from the RC intensity data. The purple colorrepresents the increase of the intensity values after the RC and thebrown color represents the reduction of the intensity values afterthe RC. It is found that the changes of the intensity values of theground features at nadir is relatively smaller than the changes ofthe intensity values of the features at the edges of the scanningarea (north and south part of the image). This can be justifieddue to the increase in the range at the edges compared to the rangeat nadir. For features with sharp slant surfaces such as edges ofbuildings and the tree clusters in the west part of the image, highdifferences of intensity values can be found. This is mainly due tothe high value of hr (the angle between the incidence laser beamand the surface normal of the reflected ground object) in Eq. (2)resulting a high value of spectral reflectance determined fromthe radar (range) equation. Generally, high differences of intensityvalues are observed on the vegetation areas when compared to thebuilt-up features.

The intensity values of homogeneous samples (training signa-tures) of different land cover features are selected in order to com-pare statistically the intensity values of the training signaturesbefore and after the RC. The purpose is to create appropriate landcover training signatures for the intensity data image classification

e of Technology, Vancouver, BC, Canada.

Fig. 4. The original intensity data (Left) and the intensity data after radiometric correction (Right).

Fig. 5. The changes in intensity data presented by differences of the digital numbers of corresponding pixels of the image intensity data before and after radiometriccorrection.

40 W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44

and to investigate the separability of the intensity values amongstdifferent land cover classes before and after the RC. Fig. 6 showsthe changes of the intensity values of the training signatures takenfor five different land cover features. The range of the intensity val-ues of different land cover features before and after the RC is plot-ted. It is clear that building and road features have relatively lowintensity values comparing to vegetation areas. However, there isan overlap between the intensity values of vegetation and build-ings before the RC. A significant separability between the intensityvalues of vegetation and buildings is recorded after the RC.

The RC process has also a significant impact on the intensityvalues recorded for the natural features particularly the inten-sity values of vegetation areas. The seperabilities amongst theintensity values of tree, soil, and grass are significantly increasedafter the RC. Before correction, overlap between intensity valuesof grass and soil and intensity values of soil and tree can be found.After applying the RC, the intensity values of these three land coverfeatures do not have any overlap. One should note that the reasonof high standard deviation (SD) of the intensity values of the treefeatures after RC can be explained by the complexity of modellingthe leaf orientation for laser reflection angle in the RC process.Although the SD value of tree features is high, this does not causeany overlap of intensity values amongst the other land cover

features as the SD values of grass and soil are reduced. Based onthe intensity values from the samples, the RC process can enhancethe separability of the land cover features which can help to im-prove the classification process.

4.2. Impact of the geometric calibration and radiometric correction ofLiDAR data on land cover classification

Several classification scenarios of different number of land cov-er classes were conducted on the LiDAR intensity data before andafter the GC and RC to evaluate their impact on the classificationaccuracy. The classes design follows the standardized nationalLand Cover Classification Scheme (LCCS) from the United StatesGeological Survey (USGS) (Anderson et al., 1976). The first scenarioclassifies the study area into three land cover classes: (i) urban orbuilt-up land (Class 1 in Level I of USGS LCCS), (ii) trees, and (iii)grass land. The second scenario comprises four classes: (i) urbanor built-up land, (ii) trees, (iii) grass land, and (iv) barren land(Class 7 in Level I of USGS LCCS) which is described as an area ofthin soil, sand, or rocks where less than one-third of the area hasvegetation or other cover (Anderson et al., 1976). The last scenariocontains five classes after subdividing the urban or built-up classinto roads and buildings (Level II of USGS LCCS). The five land cover

Fig. 6. Comparison of intensity values of different feature classes before and after the radiometric correction.

Table 1Design of the land cover classes for experimental testing.

3-Classes 4-Classes 5-Classes

1. High rangeland (tree) 1. High rangeland (tree) 1. High rangeland (tree)2. Low rangeland (grass) 2. Low rangeland (grass) 2. Low rangeland

(grass)3. Barren land (soil) 3. Barren land (soil)

3. Urban or built-upland

4. Urban or built-upland

4. Building

5. Road

Table 2Kappa statistics of individual land cover classes.

Land coverclass

Originalintensity data

GCintensitydata

RCintensitydata

GC and RCintensity data

1. Tree 0.1518 0.1007 0.6096 0.66202. Grass 0.4638 0.4693 0.3572 0.39633. Built-up

land0.4264 0.4505 0.6462 0.6096

1. Tree 0.0909 0.0480 0.6154 0.67812. Grass 0.2540 0.3213 0.1798 0.35683. Barren

land(soil)

0.1520 0.1408 0.2974 0.2385

4. Built-upland

0.4264 0.4505 0.7032 0.6536

1. Tree 0.0909 0.0480 0.6154 0.67812. Grass 0.2547 0.3213 0.1798 0.35683. Barren

land(soil)

0.1529 0.1408 0.2980 0.2597

4. Building 0.0278 0.0357 0.1285 0.10515. Road 0.3407 0.3416 0.3975 0.4112

Table 3Overall accuracy of classification results.

Dataset 3-Classes (%) 4-Classes (%) 5-Classes (%)

Original intensity data 60.50 51.78 30.50GC intensity data 60.59 53.17 32.08RC intensity data 68.52 62.08 42.08GC and RC intensity data 69.90 63.47 43.27

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classes are as follows: (i) roads, (ii) buildings, (iii) trees, (iv) barrenland, and (v) grass land. Table 1 summarizes the three classificationscenarios and their corresponding land cover classes.

Training sites were selected on the intensity data with the aid ofthe available true ortho-rectified aerial image for the three classi-fication scenarios. Identical training sites were applied to the fol-lowing four datasets: (a) the original intensity data, (b) the GCintensity data, (c) RC intensity data, and (d) the GC and RC intensitydata. Gaussian maximum likelihood classification (MLC) techniquewas used for intensity data image classification. 1010 randomcheckpoints were generated and verified visually by comparingthe classification results and the ortho-rectified aerial image to

assess the classification accuracy. The kappa statistics (see Table 2)and the overall accuracy (see Table 3) of the classification resultswere calculated for the three classification scenarios and the fourdatasets. Fig. 7 shows the classification results for the three scenar-ios from original intensity data and GC and RC intensity data.

Generally, the results presented in Fig. 7 shows better classifica-tion results when the GC and RC intensity data is used. This obser-vation is verified by the results obtained from the calculations ofthe kappa statistics and the overall accuracy. The kappa statisticsof almost all the land cover classes are increased after GC and RC(see Table 2). The major difference of the classification results be-tween the datasets is in the tree class. In the three classificationscenarios, the tree clusters in the west of the study area are mis-classified as urban built-up area or soil using the original intensitydata. This can also be seen at individual trees located along themain roads. The kappa statistics of the tree class increases from0.1 to above 0.6 in all the three scenarios after applying GC andRC. In addition, the kappa statistics of the built-up land increasesfrom 0.4 to 0.6 in the 3- and 4-classes scenarios. Although the kap-pa statistics of roads and buildings are dropped in the 5-classesscenario, slight improvement can also be found after GC and RC.The drop of the kappa statistics from 4-classes (built-up land) to5-classes (road and building) can be explained by the spectral mix-ture of the materials of building roofs and paved roads. This can befurther improved if the elevation data is incorporated in the classi-fication process and this will be considered in the future work.

By analyzing the overall accuracy presented in Table 3, the clas-sification results of the datasets with GC and/or RC is always higherthan the results from the original dataset. For 3-classes scenario,the accuracy of all classification results was over 60%. The classifi-cation results of GC intensity data is almost the same as the origi-nal intensity data; however, the overall accuracy increases 8.0%and 9.4% by classifying the RC intensity data and GC and RC inten-sity data, respectively. Although the overall accuracy of the classi-fication results from all the dataset drops for the 4-classes to5-classes scenario, improvement of the classification accuracy isrecorded by using the GC intensity data, RC intensity data, andRC and GC intensity data. In the 4-classes scenario, the accuracy

Fig. 7. (a) Ortho-rectified aerial photo; classification results of original intensity data for (b) 3-classes, (d) 4-classes, and (f) 5-classes; and classification results of GC and RCintensity data for (c) 3-classes, (e) 4-classes, and (g) 5-classes.

42 W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44

Fig. 8. Accuracy improvements of the land cover classification after geometric calibration and/or radiometric correction.

W.Y. Yan et al. / ISPRS Journal of Photogrammetry and Remote Sensing 67 (2012) 35–44 43

of the classification results of the original intensity data is close to52% while 10.2% and 11.7% improvement are recorded after usingthe RC data, and GC and RC data, respectively. In the 5-classes sce-nario, the overall accuracy of the original dataset is just above 30%;the RC demonstrates significant impact on the classification resultsby 11.6% to 12.8% of accuracy improvement using the RC intensitydata and GC and RC intensity data, respectively. It is also noted thatthe GC only has an impact on the classification accuracy by 1.4–1.6% improvement in the 4-classes to 5-classes scenarios. Fig. 8summarizes the overall accuracy improvement of the classificationresults which is determined by subtracting the percentage of theoverall classification accuracy of the original intensity data fromthe percentage of the overall classification accuracy after applyingthe GC and/or the RC on the intensity data. The results obtaineddemonstrate that both GC and RC have a positive impact on theaccuracy of land cover classification.

5. Conclusions and future work

This paper discusses the effects of the geometric calibration andradiometric correction of the LiDAR intensity data on the land cov-er classification accuracy. The GC procedure is denoted as the qua-si-rigorous method which is appropriate for linear LiDAR scanner.This method requires time-tagged point cloud and trajectory posi-tion data where the access to this data type is not a concern. Thegeometric system calibration can provide value-added information(i.e., improved scan mirror angles and ranges) for the RC of the Li-DAR intensity data when system raw measurements are not avail-able. A physical model based on the radar (range) equation is usedfor RC of the intensity data. The correction considers the systemparameters, the topographic effect, and the atmospheric attenua-tion. After applying GC and RC, the separabilities amongst trainingareas of the land cover classes in terms of intensity values are high-er than those from the original intensity data. In this regard, accu-racy improvement can be expected when using the GC and RCintensity data for land cover classification.

Classification results of the original intensity data, the GC inten-sity data, the RC intensity data, and GC and RC intensity data areassessed and compared for different scenarios of 3- to 5-featureclasses. The classification accuracy of the original intensity dataranges from 31% to 61% where the classification accuracy of theGC and RC intensity data ranges from 43% to 70%. It is found thatthe classification accuracy of the intensity data is improved by0.1% to 1.6% after GC, by 8.0% to 11.6% after RC, and by 9.4% to12.8% after GC and RC. The results indicate that GC and RC shouldbe implemented for airborne LiDAR intensity data to improve theclassification accuracy.

The work presented will further be improved by incorporatingaerial images and DSM for the land cover classification. Futurework of the radiometric modelling will focus on other factorsincluding the automatic gain control factors, the atmospheric tur-bulence effect, and cross calibration of multiple strips LiDAR inten-sity data. Interpolation techniques and tailor-made classifier willbe developed for the LiDAR intensity data classification. The re-search will further propose a comprehensive physical model forthe correction and calibration of the airborne LiDAR intensity data(both for multi-echo and full-waveform) in order to maximize thebenefit of using the intensity data for surface classification and ob-ject recognition applications.

Acknowledgements

This research work is supported by Discovery Grants from theNatural Sciences and Engineering Research Council of Canada(NSERC) and the GEOIDE Canadian Network of Excellence, StrategicInvestment Initiative (SII) project SII P-IV # 72. The authors wouldlike to thank McElhanney Consulting Services Ltd, BC, Canada forproviding the real LiDAR and image datasets. Also, the authorsare indebted to Mr. Dan Tresa, McElhanney Consulting ServicesLtd., for the valuable feedback.

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