AbstractWall painting plays an important role in the culture relics ofthe Mogao Caves in Dunhuang, P.R. China. A novelapproach of generating a high-resolution orthoimage of thewall painting is proposed. Since the photographic object isnearly flat and also the forward overlap between adjacentimages is smaller than 60 percent, the main difficulty to beresolved is the high correlation problem among theunknowns. Improved models of relative orientation andbundle adjustment with virtual constraints have beendeveloped to resolve the high correlation problem. A Voronoidiagram of projective footprints is applied to automaticallydetermine the mosaic lines of ortho-rectified images. Thecolor quality of the generated orthoimage is improvedthrough global minimization of the color differences amongoverlapped images. The experimental results show that theproposed approach has great potential for conservation ofwall paintings with sub-millimeter to millimeter precision.

IntroductionThe Mogao Caves in Dunhuang are the largest and oldesttreasure house of Buddhist art in the world. There are about500 caves where wall paintings are located, and they are themost important elements of the culture information of theserelics. However, the Mogao Caves are being damaged byrock erosion and serious natural disasters as well as thenegative effects of the increasing number of tourists at thesite. Traditional protection methods, such as copy, photogra-phy, and videography, are not meeting the high precisionprotection requirement. It is now an urgent task to find thekey high precision technologies that are capable of conserv-ing the wall paintings.

In recent years, close-range photogrammetry and 3D laserscanning have been the most popular techniques for heritageconservation (Stylianidis et al., 2005). Digital images haveseveral advantages in photogrammetric applications, such as amature working flow of data processing, a high potentialfor automation, and good geometric accuracy; while at thesame time, the recent progress in terrestrial laser scanning

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APPLICATIONS PAPER

Precise Orthoimage Generation of DunhuangWall Painting

Yongjun Zhang, Zuxun Zhang, Mingwei Sun, and Tao Ke

technology now allows fast and efficient collection of 3D coor-dinates for cultural heritage preservation (Naci, 2007). Adrawback of the laser scanner, however, is that it remainsexpensive. The data quality is also influenced by surfaceproperties, and it is possible that there are millions of pointseven on a perfectly flat surface, which often result in over-sampling (Remondino et al., 2008). The latest laser scannersystems also cannot generate very high-resolution colorimages, which are very important for wall painting conserva-tion. Independent image data acquisition has to be performedfor the integration with laser scanner data. Precise registrationbetween point clouds and images is the key problem to beresolved in order to get reasonable orthoimage. But there is nosatisfactory technique available on the automatic registrationbetween point clouds and optical images of planner objects.

However, high-resolution images acquired by digitalcameras can be processed using image-based modelingtechniques to survey and document cultural heritage (Altanet al., 2004; Saloniaa, 2005). The image-based virtual Dun-huang Art Cave navigation system was developed by Lu andPan (2000) using computer graphics and virtual realitytechnologies. Wei et al. (2003) virtually restored the color ofancient walls using artificial intelligence techniques. Ke et al.(2008) restored a wall painting with 5 to 10 mm accuracyusing technologies of close-range photogrammetry andcomputer-based image processing. Different image processingalgorithms and robust estimation methods are combined byBarazzetti et al. (2010) to obtain accurate locations and auniform distribution of tie points in close range images.Close range orthoimage of 8 mm accuracy was achieved byTsiligiris et al. (2003) with a pre-calibrated digital camcorder.There is no information in the literature about orthoimagegeneration of wall paintings with a sub-millimeter or evenone millimeter level of geometric accuracy. However, inorder to thoroughly digitize and protect the Buddhist wallpaintings, the printed version produced with a 150 dpiprinter should be the same size as the actual wall painting.That means the required ground sample distance (GSD) of thephotographed images therefore must be better than 0.17 mm.The desired geometric accuracy is about one millimeter.

A new approach of precise orthoimage generation ofDunhuang wall paintings based on photogrammetrictechniques is proposed in this paper. The general strategiesfor orthoimage generation and the data sources used for

Photogrammetric Engineering & Remote Sensing Vol. 77, No. 6, June 2011, pp. 631–640.

0099-1112/11/7706–0631/$3.00/0© 2011 American Society for Photogrammetry

and Remote Sensing

Yongjun Zhang and Zuxun Zhang are with the School ofRemote Sensing and Information Engineering, and the State KeyLaboratory of Information Engineering in Surveying, Mappingand Remote Sensing, Wuhan University, No. 129 Luoyu Road,Wuhan, 430079, P.R. China (zhangyj@whu.edu.cn).

Mingwei Sun and Tao Ke are with and the State Key Laboratoryof Information Engineering in Surveying, Mapping and RemoteSensing, No. 129 Luoyu Road, Wuhan, 430079, P.R. China.

experiments are described in the next Section, followed bythe detailed methodologies of orthoimage generation,including image matching, bundle adjustment with virtualconstraints, ortho-rectification, and color correction. Theexperimental results of orthoimage generation are presentedfollowed by further work recommendations.

General Strategy and Data SourcesGeneral StrategyThe photogrammetric processing of close range images ofquasi-planar wall paintings, such as image matching,bundle adjustment, ortho-rectification, and color correction,are the contents of the current work. The images used forexperiments are acquired with a pre-calibrated digitalcamera fixed on a movable platform as shown in Figure 1.In order to eliminate outliers during image matching,automatic estimation of the relative orientation parametersis one of the most difficult problems, because they arehighly correlated in the case of quasi-planar scenes andsmall forward overlap stereos. To avoid the high correlationproblem and to detect gross mismatches, three parameters(by, bz, and ) are used as unknowns of relative orientationfirstly. Then and are treated as the weighted unknownstogether with the above three parameters to detect theremaining subtle outliers. The precise conjugate point isacquired by a least-squares image matching within a localsearching range predicted by the relative orientationparameters. The coordinates of the matched image pointsand measured ground control points (GCPs) are utilized asdata sources of bundle adjustment. Moreover, the quasi-planar constraints of the projection centers are added intothe bundle adjustment model. A digital surface model (DSM)is generated by filtering and interpolating the object pointsobtained through bundle adjustment. The original imagesare then ortho-rectified with the above generated DSM and

wvk

the known exterior orientation parameters. Finally, thecolor quality of the ortho-rectified image is improved usingcolor correction techniques.

Data SourcesThe Research Institute of Dunhuang started the digitizationand protection project of wall paintings several years ago.More than 20,000 images located in about 100 caves havealready been obtained with 50 percent forward overlap and50 percent sidelap a few years ago. These archived imagesare used for experimental purposes in this paper. The testwall painting used here is located on the south wall of anancient cave and measures about 6 m long, 4 m wide, and4 m high. The test wall appears to be a slightly bendedplane, and the deflection at the top of the wall is onlyabout 0.2 m. As shown in Figure 1, a mobile platform thatcan horizontally move along a fixed track utilizing gearwheels is set up in the cave for the convenience of photog-raphy. The track fixed on the ground is precisely alignedin parallel with the wall painting. A pre-calibrated CannonEOS 1Ds Mark III digital camera with a focal length of 53mm is mounted on the platform which can move up anddown vertically. Under this hardware configuration, theprojection centers of all the images are nearly coplanar,and the variations of all the projection centers against theideal vertical plane are smaller than 0.02 m.

The object coordinate system is setup as follows: theorigin is at the lower-left corner of the wall painting, the X-axis is horizontal and points to the right side, the Y-axispoints upwards, and the Z-axis points outwards from thewall. The image format and physical pixel size of thecamera are 5,616 � 3,744 pixels, and 0.0064 mm, respec-tively. The distance between the wall and the camera isapproximately 1.0 m; thus, the GSD is about 0.13 mm. Atotal of 15 strips containing 298 images with about 50percent forward overlap and 50 percent sidelap are obtainedby the camera. The view directions of all images are parallel

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Figure 1. Photographic equipment and coordinate system of the test wall painting.

to each other and perpendicular to the X-Y plane of theobject coordinate system. Figure 2 shows one of theacquired images with resolutions of 1:25, 1:8, and 1:4.In order to evaluate the precision of the orthoimage gener-ated by the proposed approach, 59 ground points aremeasured by an electronic total station. The planar andheight accuracies of these points are about 1.0 mm and 2.0mm, respectively. The 31 triangular-shaped points in Figure 3 are used as control points, while the other 28square-shaped ones are check points for the bundleadjustment in the Experiments and Results Section.

Orthoimage Generation MethodologyImage MatchingImage matching is the prerequisite of many applications incomputer vision and photogrammetry, including objectrecognition, motion tracking, bundle adjustment, and 3Dinformation extraction. In this paper, an image matchingprocess is also necessary to find conjugate points amongadjacent images, which is composed of three steps: featurepoint extraction; area-based matching, and relative orientation.

Feature point extraction is the first step in all area-basedimage matching algorithms. There are several well knownoperators, such as Moravec, Forstner, and Harris (Moravec1977; Förstner and Gülch 1987; Harris and Stephens, 1988). Inthis paper, the feature points of each image are first extractedwith the Harris detector and then refined by the Förstneroperator to obtain the necessary sub-pixel precision. Each imageis divided into rectangular grids with a pre-defined grid size

(e.g., 180 � 180 pixels). One feature point with the strongestinterest value above a given threshold is extracted from eachgrid. About 600 feature points can be extracted from each of theimages acquired by the aforementioned digital camera.

A pyramid-based matching strategy, which is usuallyadopted by traditional image matching, is also used in thispaper. Area-based matching with correlation coefficients can beperformed as long as the approximate overlap and rotationangle of a stereo pair are available. The nominal overlap issufficient for predicting the initial position of potential conju-gate points for each feature point since the photographic objectis nearly planar. Then, area-based image matching strategies,such as correlation coefficient and least-squares matching, areused to find precise conjugate points within a local rangearound the predicted position. For the purpose of connectingall of the stereo models, the matched image points in onestereo pair should be automatically transferred to all of theneighboring stereos, which can be accomplished by replacingthe extracted feature points of the current stereo with thesuccessfully matched conjugate points of the previous stereo.

In the traditional procedure of photogrammetry, relativeorientation with five unknowns can be performed as long asfive or more conjugate points are available. However, therelative orientation parameters (by and ; bz and ) areclosely correlated because the wall painting is quasi-planarand the image overlap is less than 60 percent. Usually, thecondition number of the normal matrix of relative orienta-tion with natural terrain images is on the 5.0 � e7 level,while the condition number of that with wall paintingimages is at least 5.0 � e9 and sometimes larger than 1.0 � e12. It is obviously an ill-posed problem that willresult in inconsistent relative orientation solutions. Only theby, bz, and are used as unknowns to detect gross mis-matches at the beginning of the relative orientation. Then, and are used as weighted unknowns in addition to theabove three parameters to detect the remaining subtleoutliers. In this case, the condition number of the normalmatrix is on the 1.0 � e8 level, which is much better thanthe traditional method. Least-squares adjustment with robustestimation technique is used to compute the unknowns ofrelative orientation. The calculated relative orientationparameters can be used to assist image matching by provid-ing epipolar constraints. Figure 4 shows the automaticallymatched conjugate points of six stereo images. About 200conjugate points are successfully matched between eachstereo pair. The accuracy of image matching is usually betterthan 0.3 pixels since the least squares image matchingstrategy is applied. The white crosses in the images repre-sent the conjugate points that pertain to images of a singlestrip while the black crosses represent conjugate points thatpertain to images of two different strips.

wv

k

wv

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Figure 2. Photographed original image of the test wall painting: (a) full image with 1:25 resolution, (b) partof image with 1:8 resolution, and (c) part of image with 1:4 resolution.

Figure 3. Distribution of control and checkpoints of thetest wall painting.

(a) (b) (c)

Bundle Adjustment with Quasi-planar ConstraintsBundle adjustment is the key technology of geometricanalysis and also a prerequisite of precise 3D informationextraction. Collinearity equations are the basic mathematicalmodel of bundle adjustment. However, the normal matrix ofbundle adjustment is seriously ill-posed because the photo-graphic object is quasi-planar, and moreover the overlapbetween adjacent images is only about 50 percent. Additionalconstraints must be considered in order to get reliable resultsfrom bundle adjustment. As previously described, all of theprojection centers are nearly coplanar with about 0.02 mvariation. This knowledge of the projection centers can becombined into the adjustment model to improve the geomet-ric configuration of the normal equation. Note that thecoplanar constraints of the projection centers cannot be usedas strict conditions in combined bundle adjustment. Theyshould be used rather as pseudo observations with 0.02 m apriori accuracy.

The interior parameters of the digital camera arecalibrated just before photography, so self-calibration is notnecessary for bundle adjustment. The collinearity equationsare the basic mathematic model of bundle adjustment(Mikhail, et al., 2001; Zhang et al., 2008):

(1)

where and are the observations, and the coordinatesof ground point, are the orientation matrixcomposed of rotation angles and where the Y-axis istaken as the primary axis, and arethe exterior and interior parameters, respectively.

The same height constraints of the projection centershave the following form:

(2)

where are the heights of the projection centers, and nis the number of projection centers. The combined

Zsi, Zsj

Zsi � Zsj (i � 0,1,2 Á n,i Z j)

XS, YS, ZS, w, v, k, f, x0, y0

kw, v,ai, bi, ci (i � 1,2,3)

ZX, Y,yx

y � y0 � � f a2(X � Xs) � b2(Y � Ys) � c2(Z � Zs)

a3(X � Xs) � b3(Y � Ys) � c3(Z � Zs)

x � x0 � � f a1(X � Xs) � b1(Y � Ys) � c1(Z � Zs)

a3(X � Xs) � b3(Y � Ys) � c3(Z � Zs)

bundle adjustment model is composed of Equations 1 and 2.The observation equations of the combined adjustment modelhave the following form:

(3)

where is the designed matrix of vector , and the correction vector

of observations, and constant items calculated by theapproximate values of unknowns, and and thecorresponding weight matrix. The weight matrix is veryimportant for bundle adjustment in order to obtain reliablesolutions. The image matching precision is usually about0.3 pixels. The initial weight of each image point is set to be1.0, i.e., the unit weight root mean square error (or sigmanaught) equals to 0.3 pixels in image space and 0.04 mm inobject space since the GSD is 0.13 mm. The initial weights ofpseudo observations are defined by their a priori accuraciesagainst the unit weight variance. The a priori accuracies ofthe ground control points are 1.0 mm in planar and 2.0 mmin height, respectively, while the a priori accuracy of theheights of the projection centers is 0.02 m. Note that theweights of all observations are recalculated according to theiterated results during bundle adjustment.

Ortho-rectification and Color CorrectionDSM, usually generated by image matching with extracteddense features, is an essential data source to generateorthoimages. In this paper, dense image matching is notnecessary because the wall is quite smooth. DSM can begenerated by filtering and interpolating the object points ofaerial triangulation. Then, ortho-rectification can be per-formed image by image. The Voronoi diagram of the projec-tive footprints is used to automatically generate mosaic linesof all the separately rectified orthoimages. As shown inFigure 5, the five white crosses are the planar positions ofthe projective centers, and the five corresponding polygonsrepresent the automatically generated mosaic lines. Eachclosed polygon is filled with one ortho-rectified image.Therefore, the whole orthoimage of the wall painting isautomatically generated after all of the polygons have beenfilled with corresponding orthoimage blocks.

PzsPWzsl

VzsVdXs, dYs, dZs, dw, dv, dk)dT � (dX, dY, dZ,B

Vzs � dZsi � dZsj � Wzs Pzs

V � BdT � l P

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Figure 4. Automatically matched conjugate points among adjacent images.

The illumination of photography is provided by specialtubular lighting equipment. A standard color calibration targetis used during image acquisition since the imagery is intendedfor conservation. However, the lighting conditions and theperformance of the camera’s auto-white balance functionoccasionally change slightly, and the photographed imagesoften have different color hues, especially those belonging todifferent strips. These conditions will result in significantdifferences of color hues in the mosaicked orthoimage. Figure6 shows an orthoimage block mosaicked by five adjacentimages. Note that the lower part of Figure 6a has apparentcolor differences when compared to the upper part.

It is apparent that color correction must be performed togenerate an orthoimage with uniform color hues. The basicidea of color correction is that the overlapped areas ofadjacent images should have the same color hues. The firststep of color correction is the calculation of the color huesof the overlapped areas among the adjacent images. Sixcorrection parameters are then introduced for the three RGBchannels of each image. Each channel has two correctionparameters: the mean and the standard deviation of grayvalues. The color differences among the adjacent images are

globally minimized by calculating the correction parametersof each image. Finally, the color of each ortho-rectifiedimage is corrected according to the calculated parametersand the color-corrected images are mosaicked together asshown in Figure 6b. It is obvious that the proposedapproach significantly improves the color quality.

Experiments and ResultsSeveral sets of close range digital images and ground controlpoints are used as sources of information for the experi-ments of the proposed approach. The experimental resultsare similar for all of the test data sets. Experimental resultsof image matching, bundle adjustment, digital orthoimagegeneration, and color correction of one of the wall paintingswill be discussed in this Section.

Image MatchingRelative orientation is performed to calculate the five parame-ters and to detect the outliers, provided that five or moreconjugate points are successfully matched. Then, an epipolarline, which can be calculated from the relative orientation

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Figure 5. Automatically generated mosaic lines of adjacent orthoimage blocks.

Figure 6. Mosaicked orthoimage block before and after color correction: (a) before color correction, and(b) after color correction.

(a) (b)

parameters, is used to reduce the searching range for imagematching. There are usually 200 to 300 successfully matchedconjugate points between each image pair with the proposedimage matching strategy. The sigma naught of the relativeorientation of both the forward overlap and sidelap stereos isusually in the range of 0.0015 mm to 0.0022 mm (i.e., one-fourth to one-third physical pixels). The crosses in Figure 7are the automatically matched conjugate points of twoforward overlap stereo pairs. As can be seen, all of theconjugate points are randomly distributed on the overlappedarea. The conjugate points of the adjacent strip stereos arealso very important for aerial triangulation because they canlink different strips together. Figure 8 shows the automati-cally matched conjugate points of two sidelap stereo pairs.Similar to that of Figure 7, nearly no outliers remain amongthe conjugate points after relative orientation. The overallcorrectness rate of the matched conjugate points is higherthan 98 percent, which means that less than two percent ofthem are possibly mismatched. These excellent results showthat the proposed image matching algorithm is feasible tomatch image pairs of wall paintings.

The general overlaps and relative rotation anglesbetween adjacent images can be calculated from the relativeorientation parameters. The forward overlap of the testimage data is in the range of 50 to 55 percent, while thesidelap is in the range of 48 to 52 percent. The maximumrelative rotation angle between adjacent images is less thantwo degrees, which is the benefit of the photographicequipment shown in Figure 1.

Bundle AdjustmentBundle adjustment with ground control points is performedin order to obtain precise exterior orientation parametersand thus generate a reasonable orthoimage of the wall

painting for cultural heritage protection. 15,932 automati-cally matched conjugate points and 59 manually measuredground control points are used as data sources. As shown inFigure 3, 31 of the 59 points are used as GCPs, and the other28 are checkpoints.

The interior orientation and lens distortion parametersare treated as known during bundle adjustment since thecamera is precisely calibrated in advance. Two experimentsof bundle adjustment are performed. The first is the tradi-tional bundle adjustment without virtual constraints, whilethe second has virtual constraints as previously described.The unit weight root mean square (RMS) error of the twobundle adjustments are 0.0038 mm and 0.0033 mm, respec-tively. Error statistics of GCPs and checkpoints are shown inTable 1 and Table 2, respectively. As can be seen fromTable 1, the RMS error of the planar position residues ofthe GCPs and checkpoints are both about 0.70 mm; whilethe RMS of the height residues of the check points is 4.06mm, which is nearly two times of that of the GCPs.Furthermore, the maximum height residues of the GCPs andcheckpoints are 4.86 mm and �7.77 mm, respectively. Theresult of the traditional bundle adjustment model is clearlydeformed due to the poor geometric configuration. How-ever, once the virtual constraints are combined into thebundle adjustment model, the result becomes quite accept-able. As shown in Table 2, the RMS errors of the planarposition residues of the GCPs and checkpoints are slightlybetter than those of the first experiment, while the RMSerrors of the height residues of the GCPs and check pointsare both at the 1.50 mm level. The maximum height residueis only about 2.10 mm, which is much better than that ofthe first experiment.

Although the achieved result of the second bundleadjustment is not comparable to the ground resolution of

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Figure 7. Matched conjugate points of two forward overlap stereo pairs.

imagery, it is reasonable since the control and check-points are measured by an electronic total station with a1 mm � 1 ppm nominal accuracy of distance measurement.One can imagine that the bundle adjustment result will besignificantly improved if more precise control and check-points are available.

Ortho-rectification and Color CorrectionOnce aerial triangulation is finished, a DSM can be generatedby filtering and interpolating the obtained object point

coordinates from aerial triangulation. As shown in Figure 9,the interpolated DSM of the test wall painting, which isproduced by commercial software DPGrid (Zhang et al.,2009), is quite smooth. Digital orthoimages can be obtainedthrough ortho-rectification of the original images with thecamera parameters and the interpolated DSM. The mosaiclines of all the ortho-rectified images are automaticallycalculated by the Voronoi diagram of the projective footprints. Plate 1a shows the mosaicked digitalorthoimages of the test wall painting. As can be seen, thereare apparent color differences among images of differentstrips in the lower part.

Two color correction parameters for each channel of acertain image are introduced to compensate for the colordifferences among the images. A total of 1,788parameters are introduced for the 298 images. Each imageis corrected by Wallis filtering (Mastin, 1985) with theestimated parameters of the desired mean andstandard deviation. Plate 1b shows the final restoredorthoimages of the test wall painting after color correction.

The precision of the generated orthoimage is verifiedby the GCPs and checkpoints that are used in bundleadjustment. The planar coordinates of the GCPs andcheckpoints are interactively measured from the orthoim-age and then compared with the ground truth. The RMSerror of differences is smaller than 1.0 mm. In order tofully evaluate the precision of the generated orthoimage,an external verification is performed. The coordinates ofseven randomly selected feature points on the actual wallpainting are measured by the on-site electronic totalstation. The horizontal distances between an arbitrary twopoints are calculated by their world coordinates andcompared with those measured from the orthoimage. The

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TABLE 1. RESIDUES OF CONTROL AND CHECKPOINTS OF BUNDLE ADJUSTMENTWITHOUT CONSTRAINTS (MM)

Item RMS Mean Max/Min

Control Points X 0.35 0.05 0.82Y 0.54 0.07 �2.03Z 2.12 0.16 4.86

Check Points X 0.42 �0.11 0.77Y 0.60 0.12 1.36Z 4.06 �0.06 �7.77

TABLE 2. RESIDUES OF CONTROL AND CHECKPOINTS OF BUNDLE ADJUSTMENT WITH CONSTRAINTS (MM)

Item RMS Mean Max/Min

Control Points X 0.27 0.03 0.60Y 0.49 0.04 1.05Z 1.24 0.06 2.05

Check Points X 0.38 �0.05 �0.81Y 0.54 0.03 1.03Z 1.46 �0.04 2.09

Figure 8. Matched conjugate points of two sidelap stereo pairs.

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Plate 1. Digital orthoimage of the test wall painting: (a) before color correction, and (b) after color correction.

maximum difference is also around 1.0 mm. Theseexternal checks further verify the geometric accuracyof the generated orthoimage. In fact, the internalgeometric accuracy of the generated orthoimage shouldbe on the sub-millimeter level because photogrammetryconsistently generates an orthoimage with precisioncomparable to ground resolution. The millimeter level precision of the experiment is mainly determined by the accuracy of the GCPs. Further investigationis planned to verify this assumption in the near future.

ConclusionsA new approach of precise orthoimage generation for aquasi-planar object with close range images is discussed inthis paper. The difficulties are that the photographic objectis nearly flat, and the forward overlap is smaller than 60percent, which will significantly influence the stability andreliability of the relative orientation and bundle adjustment.The pseudo observations of the coplanarity constraints ofthe camera translation parameters used in this paper arevital to improve the geometric configuration. The experi-mental results show that the proposed image matchingstrategy can automatically locate sufficient conjugate pointsfrom both the forward overlap and the sidelap image pairs.Pseudo observations of the constraints used in bundleadjustment significantly benefit the geometric configurationand thus improve the height precision. The geometric andcolor quality of the final orthoimage generated are satisfyingfor wall painting protection.

An objective color quality evaluation of the generatedorthoimage is the main work planned in the near future.Improvement of the image acquisition equipment andmeasurement of the GCPs with higher accuracy also need tobe investigated to generate more precise orthoimages ofwall paintings.

AcknowledgmentsThis work is supported by the National Natural ScienceFoundation of China with Project No. 41071233, 40671157,the National Key Technology Research and DevelopmentProgram with Project No. 2011BAH12B05, and the Programfor New Century Excellent Talents in University with ProjectNo. NCET-07-0645. I am very grateful also for the commentsand contributions of anonymous reviewers and members ofthe editorial team.

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(Received 02 December 2010; accepted 28 January 2011; finalversion 15 February 2011)

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