Preparing for the Mobile FuturePreparing for the Mobile Future

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1 Processing of Ikonos imagery for submetre 3D positioning and

2 building extraction

3 C.S. Fraser a,*, E. Baltsavias b, A. Gruen b

4 aDepartment of Geomatics, University of Melbourne, Melbourne, 3010 Victoria, Australia

5 b Institute of Geodesy and Photogrammetry, ETH-Hoenggerberg, CH-8093 Zurich, Switzerland6

7 Abstract

8 An investigation of the application of 1-m Ikonos satellite imagery to 3D point positioning and building reconstruction is

9 reported. The focus of the evaluation of Geo panchromatic imagery has been upon 3D positioning accuracy, radiometric quality

10 and attributes of the image data for building feature extraction. Following an initial review of characteristics of the Ikonos

11 system, the multiimage dataset employed is described, as is the Melbourne Ikonos testfield. Radiometric quality and image

12 preprocessing aspects are discussed, with attention being given to noise level and artifacts, as well as to methods of image

13 enhancement. The results of 2D and 3D metric accuracy tests using straightforward geometric sensor models are summarised,

14 these showing that planimetric accuracy of 0.3–0.6 m and height accuracy of 0.5–0.9 m are readily achievable with only three

15 to six ground control points. A quantitative and qualitative assessment of the use of stereo Ikonos imagery for generating

16 building models is then provided, using the campus of the University of Melbourne as an evaluation site. The results of this

17 assessment are discussed, these highlighting the high accuracy potential of Ikonos Geo imagery and limitations to be considered

18 in building reconstruction when a comprehensive and detailed modelling is required. D 2002 Elsevier Science B.V. All rights

19 reserved.20

21 Keywords: Ikonos; Radiometric analysis; Sensor models; 3D point positioning; Building extraction

222324 1. Introduction

25 Although it has now been 2 years since Ikonos

26 imagery first became commercially available from

27 Space Imaging, there have been relatively few accounts

28 presented of photogrammetric analysis of this new

29 image data. A number of factors have contributed to

30 the current paucity of research outcomes related to the

31 geometric and radiometric quality of 1-m panchro-

32 matic, 4-m multispectral and 1-m pan-sharpened Iko-

33nos imagery. These include significant early delays in

34image delivery, restrictions regarding both the provi-

35sion of stereo imagery and disclosure of the camera

36model and orbit ephemeris data, limited data availabil-

37ity and delays due to ongoing improvements within the

38image preprocessing phase. Also, it seems fair to say

39that teething problems were encountered with what is a

40commercially new technology being made available to

41the user community under a distinctly new business

42model (e.g. Gruen, 2000).

43Published reports to date on photogrammetric as-

44pects of Ikonos imagery have focussed mainly on the

45accuracy attainable in ortho-image generation (e.g.

46Davis and Wang, 2001; Kersten et al., 2000; Toutin

47and Cheng, 2000; Toutin, 2001; Baltsavias et al., 2001;

0924-2716/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0924 -2716 (02 )00045 -X

* Corresponding author. Tel.: +61-3-8344-4117; fax: +61-3-

9347-2916.

E-mail address: [email protected] (C.S. Fraser).

www.elsevier.com/locate/isprsjprs

ISPRS Journal of Photogrammetry & Remote Sensing 1209 (2002) xxx–xxx

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48 Jacobsen, 2001) and to a lesser degree on DTM ex-

49 traction (Toutin et al., 2001; Muller et al., in press),

50 rather than upon accuracy aspects of feature extraction.

51 Results concerning 3D positioning from stereo spatial

52 intersection utilising a rigorous sensor orientation

53 model are both few and mainly from Space Imaging

54 (Dial, 2000; Grodecki and Dial, 2001), the results

55 reported being more accurate than Ikonos product

56 specifications would suggest. Investigations into 3D

57 positioning using alternative models have recently

58 been reported by Toutin et al. (2001) and Hu and Tao

59 (2001).

60 As an initial investigative phase into the application

61 of high-resolution spaceborne imagery for object fea-

62 ture point detection, recognition and reconstruction,

63 early research had been performed using simulated data

64 with empirical analysis. Ridley et al. (1997) evaluated

65 the potential of 1-m resolution satellite imagery for

66 building extraction, and findings were that only 73%

67 and 86% of buildings could be interpreted correctly

68 using monoscopic and stereoscopic imagery, respec-

69 tively. More recently, Sohn and Dowman (in press)

70 reported an investigation into building extraction from

71 high-resolution imagery. However, the study dealt with

72 large detached buildings only and a comprehensive

73 analysis of accuracy and completeness in the modelling

74 of structure detail was not performed. In a broader

75 feature extraction context, Hofmann (2001) has re-

76 ported on 2D detection of buildings and roads in Ikonos

77 imagery using spectral information, a DSM derived

78 from laser scanning, and image context and form via

79 the commercial image processing package eCognition.

80 Dial et al. (in press) have presented the results of

81 investigations into automated road extraction, with

82 the focus being uponwide suburban roads in expanding

83 US cities, while experiences with semiautomatedmeth-

84 ods are reported in Gruen (2000).

85 A feature common to almost all published work to

86 date on geometric processing of Ikonos imagery has

87 been the use of ground control of insufficient accuracy

88 to exploit the full metric potential of Ikonos Geo

89 imagery. Given the present shortage of information

90 on the performance of the Ikonos system for high-

91 accuracy 3D positioning and feature extraction, it has

92 been necessary in the reported investigation to examine

93 essentially three salient aspects when evaluating the

94 use of 1-m stereo imagery for 3D point positioning and

95 building extraction in support of 3D city modelling.

96These comprise the geometric accuracy of 3D position-

97ing from stereo and multiimage coverage; the radio-

98metric quality, with an emphasis on characteristics to

99support automatic feature extraction (e.g. noise con-

100tent, edge quality and contrast); and attributes of the

101imagery for the special application of building extrac-

102tion and visual reconstruction. Here, we examine these

103aspects with the aid of threefold Ikonos coverage of a

104precisely surveyed testfield covering the city of Mel-

105bourne. We first describe this testfield dataset and then

106examine radiometric and image preprocessing aspects.

107This is followed by an evaluation of the metric potential

108of Ikonos mono, stereo and three-image coverage,

109along with an account of experimental application of

110stereo imagery to building reconstruction, including

111topology building and visualisation.

112The basic sensor and mission parameters for the

113Ikonos-2 satellite are provided at Space Imaging’s web

114site (www.spaceimaging.com) and visitors to the site

115will note that there are basically five product options for

116panchromatic, multispectral and pan-sharpened 1-m

117Ikonos imagery: Geo, Reference, Pro, Precision and

118Precision Plus. Except for the Geo product, all are

119ortho-rectified using a DTM,with ground control being

120required for Precision and Precision Plus ortho-

121imagery. The absolute planimetric positioning accura-

122cies (RMS, 1r) associated with these categories of

123imagery are 24, 12, 5, 2 and 1 m, respectively. The

124error budget for Geo imagery does not include influ-

125ences due to terrain relief, or possible additional errors

126due to projection of the imagery on an ‘‘inflated’’

127ellipsoid at a selected elevation (this elevation value

128was not provided in the metadata file until mid-2001).

129Depending on the intended function of a digital city

130model, metric accuracy expectations will vary, though

131if the very sizeable market of modelling for mobile

132communications is to be accommodated, accuracies at

133the metre level are generally sought. The clear impli-

134cation from the product range listed above, however, is

135that users of Ikonos imagery who seek metre-level

136accuracy will have to resort to acquisition of Precision

137and Precision Plus imagery, which is 5–10 times more

138expensive than Geo. We have pursued an alternative

139option, with the aim of yielding high accuracy results,

140to metre or submetre level, using the least expensive

141Ikonos data, namely Geo images, coupled with alter-

142native computational schemes for mono, stereo and

143multiimage point positioning.

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144 2. Input data

145

146 2.1. Image data

147 The imagery comprised a stereopair of epipolar-

148 resampled Geo panchromatic images and a nadir-

149 looking scene of panchromatic and multispectral

150 imagery. The latter were also combined to form a

151 pan-sharpened image. As indicated in Table 1, the

152 sensor and sun elevation angles for the stereopair,

153 imaged in winter, were less than optimal. Apart from

154 the right stereo image, the azimuths of sensor and sun

155 differed considerably, leading to strong shadows in

156 nonoccluded areas. The overall geometry was close to

157 that of three-line imagery, with the base/height ratio of

158 the stereopair being B/H=1.2. Supplied with the stereo

159 imagery were coefficients of the rational functions that

160 provide a mechanism for object-to-image space trans-

161 formation and 3D point positioning. There were ini-

162 tially no rational function coefficients (also termed

163 RPCs) for the near-nadir image, since the option of

164 obtaining these for mono Geo imagery was not avail-

165 able at the time the data was acquired. Although these

166 were subsequently obtained, the present discussion of

167 RPC-based 3D spatial intersection is restricted to the

168 stereopair of Geo images. Features of three-image

169 RPC spatial intersection are discussed in Fraser et al.

170 (in press).

171

172 2.2. Melbourne Ikonos testfield

173 Covering an area of 7�7 km over central Mel-

174 bourne, with an elevation range of less than 100 m, the

175 testfield configuration considered comprises an array

176 of 32 GPS-surveyed ground control points (GCPs), all

177 being road roundabouts. The locations of the GCPs are

178shown in Fig. 1. As mentioned, feature extraction

179accuracies to metre level were sought from the stereo

180Ikonos data and, thus, there was a need to measure

181image-identifiable ground points to subpixel accuracy

182within the imagery, as well as to submetre precision on

183the ground. Road roundabouts were selected as control

184points since they constituted an elliptical target which

185was usually well contrasted against its background (the

186surrounding road) within the image, and which was

187typically 8–25 pixels in diameter. Within the imagery,

188it was the centroid of the roundabout that was measured

189and so the GPS survey needed also to determine the

190centre point coordinates. As described in Hanley and

191Fraser (2001), the centroids of each of the roundabouts

192were determined by measuring six or more edge points

193around the circumference of the feature, both in the

194image and on the ground, and then employing a best-

195fitting ellipse computed by least-squares to determine

196the ellipse centres. It was possible through this means

197to achieve accuracies of image and object point deter-

198mination, in 2D and 3D space, respectively, to better

199than 0.2 pixels. Fig. 2 shows one of the roundabouts as

200recorded in the nadir-looking Ikonos image, along with

201a best-fitting ellipse to its edge points.

202To complement the image mensuration via ellipse

203fitting, least-squares template matching was also

204employed within the stereo imagery, with special care

205being taken to alleviate problems such as target occlu-

206sions from shadowing and the presence of artifacts

207such as cars (e.g. Fig. 2). The matching utilised gra-

208dient images instead of grey values and observational

209weights were determined from the template gradients,

210i.e. only pixels along the circular template edge were

211used in matching, thus, making the method insensitive

212both to grey level variations within the roundabouts

213and to disturbances outside the roundabout perimeter.

214Three different template sizes were employed to ac-

215commodate the greatly varying roundabout sizes and

216in most cases an affine or conformal transformation

217was used. In spite of significant disturbances, and size

218and shape variations of roundabouts, the matching

219performed correctly in ca. 85% of the cases. Since

220the same template was matched simultaneously using

221the same parameters in all images, the image measure-

222ments were mutually quite consistent. This meant that

223parallax errors were reduced and height estimation

224accuracy could be expected to surpass that of the

225ellipse fitting method. The images used for measure-

t1.1 Table 1

Acquisition parameters for the 1-m Ikonos Geo panchromatic images

of the Melbourne testfieldt1.2

Left stereo Right stereo Nadirt1.3

Date, time (local) 16/7/2000,

09:53

16/7/2000,

09:53

23/3/2000,

09:58t1.4Sensor azimuth (j) 136.7 71.9 143.0t1.5Sensor elevation (j) 61.4 60.7 83.4t1.6Sun azimuth (j) 38.2 38.3 50.0t1.7Sun elevation (j) 21.1 21.0 38.0t1.8

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226 ment were initially preprocessed for noise reduction

227 and edge enhancement, as described in Section 3. An

228 example of the modified least-squares matching is

229 indicated in Fig. 3. The top row of the figure shows

230 the template and the two stereo images with the

231 starting (light) and final (dark) positions after a con-

232 formal transformation. The bottom row displays the

233 matched gradients. This example illustrates the ro-

234bustness of the process in the presence of both contrast

235variations and disturbances along the roundabout

236edge.

237A second essential component of the Melbourne

238testfield in the context of city modelling comprised

239information on buildings. Due to both accessibility

240constraints and the unavailability of height data to

241submetre accuracy for central city high-rise buildings,

Fig. 1. Ikonos Geo panchromatic nadir image of Melbourne showing the distribution of control points and the University of Melbourne campus.


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242 many over 30 stories, it was decided that the campus of

243 the University of Melbourne would serve as the test site

244 for the building reconstruction and visualisation phase

245 of the project. The campus comprises a few dozen

246 major buildings which vary in design characteristics,

247 size, shape, roof superstructure and height, though

248 most are between four and 10 stories tall. Building

249 height data for 19 image-identifiable and readily acces-

250 sible roof corners was provided to 10 cm accuracy

251 through precise GPS surveys. This data formed the

252 metric standard by which the 3D spatial intersection for

253 building extraction would be assessed. Image coordi-

254nate observations to these feature points were carried

255out by manual recording, both in stereo and mono-

256scopically (for the nadir image), nominally to 0.5–1

257pixel precision. However, the definition of corner

258points was often weak, leading to the possibility of

259pixel-level errors. Least-squares matching was not

260appropriate for the recording of such corner features

261due to both occlusions and geometric differences

262between the images that could not be modelled through

263affine transformation.

264An existing stereopair of 1:15,000 scale wide-angle

265colour aerial photography of the campus was employed

Fig. 3. An example of modified least squares template matching (see explanations in text).

Fig. 2. Ikonos image of roundabout (left) and recorded edge points and best-fitting ellipse (right).


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266 in the evaluation of the building extraction potential of

267 Ikonos imagery. This imagery had previously been

268 used to create a reasonably detailed 3D model of the

269 campus, which in regard to recoverable feature content

270 could be compared to that derived from Ikonos data.

271 3. Radiometric aspects and image preprocessing

272

273 3.1. Radiometric quality analysis

274 Prior to discussing radiometric features of the Iko-

275 nos imagery, a subject which is discussed further in

276 Baltsavias et al. (2001), it is noteworthy that opera-

277 tional aspects of the image acquisition are likely to have

278 a more profound effect on the homogeneity or non-

279 homogeneity of image quality than specific radiometric

280 characteristics of the sensor system. For example, large

281 changes in image quality and suitability for automated

282 feature extraction are associated with variations in the

283 sensor view angle, the sun angle and shadowing, the

284 seasons, atmospheric conditions, andwhether the scene

285 is recorded in mono or stereo. These influences are well

286 known, but it needs to be appreciated that with the

287 exception of the last aspect, they are largely beyond the

288 control of the image user. There is limited opportunity

289for the user to dictate specific imaging dates, times and

290weather conditions.

291An example of the varying quality of two Ikonos

292images (from other projects) with similar sensor ele-

293vation but varying sun illumination and atmospheric

294conditions is shown in Fig. 4. Of the three Melbourne

295images employed, the near-nadir image was superior in

296terms of both contrast and visual resolution. This can be

297explained by the higher sensor and sun elevation,

298though it is uncertain if this fact alone accounted for

299the modest difference in image quality, or whether

300differences were in addition due to changes in atmos-

301pheric conditions or aspects of the epipolar resampling

302of the stereo images.

303All images were preprocessed by Space Imaging

304with Modulation Transfer Function Compensation

305(MTFC) but no Dynamic Range Adjustment (DRA).

306Although the images were delivered as 11-bit data, the

307number of grey values having a substantial frequency

308was much less than 2048. For the left, right and nadir

309panchromatic images, grey values with a frequency of

310more than 0.01% covered only the ranges of 44–343,

31156–423 and 61–589, respectively. The corresponding

312effective grey value ranges of 299, 367 and 528 (37,

31346 and 66 grey values for a linear stretch to 8-bit) are

314quite similar to the effective intensity range observed

Fig. 4. Ikonos Geo panchromatic images of Lucerne, Switzerland (left), and Nisyros, Greece (right), exhibiting very different image quality and

building definition.


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315 with other spaceborne linear array CCD sensors such

316 as SPOT and MOMS. The peak of the histogram is

317 typically towards the darker region, with the right part

318 of the histogram decreasing smoothly and slowly

319 towards the higher intensity values.

320 The overall noise characteristics of the images

321 (also noted with Ikonos images from other projects)

322 were analysed in both homogeneous (sea surface) and

323 nonhomogeneous areas (e.g. city) by a procedure

324 explained in Baltsavias et al. (2001). The use of non-

325 homogeneous areas in image noise evaluation is jus-

326 tified as large homogeneous areas do not always exist.

327 This process also allows an analysis of the noise

328 variation as a function of intensity, as noise is not

329 additive but multiplicative (i.e. intensity-dependent).

330 The analysis revealed the presence of somewhat more

331 noise than the four grey values reported by Space

332 Imaging for raw panchromatic and multispectral

333 images without MTFC (Gerlach, 2000) and the

334 3.5–5 grey values reported using on-board calibration

335 and sun imaging (Cook et al., 2001), namely 1.5–

336 10.5 grey values depending upon intensity and spec-

337 tral type. The extent of the noise, however, was not

338 judged to be of crucial significance for the specific

339 task of building reconstruction. The noise level of the

340 multispectral imagery was much lower than that of

341 the panchromatic, with the pan-sharpened imagery

342 being slightly more noisy than the panchromatic.

343 Geo images have been found to exhibit several

344 artifacts in addition to their noise content, of which a

345 number remain unexplained. Many are visible only in

346 homogeneous areas, especially after contrast enhance-

347 ment, and some appear in areas of rich texture. The

348 artifacts often lead to small grey level variations (e.g.

349 two to four grey values in 11-bit images), although

350 after contrast enhancement to aid visual interpretation

351 and measurement, or automated computer processing,

352 they can lead to erroneous high-contrast texture pat-

353 terns. Further details are provided in Baltsavias et al.

354 (2001). The reported radiometric concerns are not

355 related solely to the sensor imaging parameters but

356 also to the subsequent image processing methods and

357 to image compression. In order to optimize the down-

358 loading of an image, large homogeneous areas are

359 compressed more, thus, leading to additional artifacts

360 in such regions.

361 TheMTFC used to reduce the image defocusing due

362 to use of time delay and integration, especially in the

363scan direction (the direction in which the lines are

364collected), can cause ringing effects and so degrade

365edge detail. The compression to 2.6 bits also leads to

366some artifacts, which are more visible in homogeneous

367areas. Some additional problems include shadows and

368image saturation. The shadow areas in the Melbourne

369imagery did not have a significant signal variation and,

370thus, in spite of a strong contrast enhancement, feature

371details in shadow areas were often barely visible. Satur-

372ation occurred routinely, with bright vertical walls,

373especially those with surface normals approximately

374in the middle of the illumination-to-sensor angle. This

375led again to a loss of detail and poor definition and even

376to the disappearance of edges when two saturated walls

377were intersecting.

378

3793.2. Image preprocessing

380In order to reduce the effects of the above men-

381tioned radiometric problems and optimise the images

382for subsequent computer processing to support the

383measurement of GCPs and buildings, various prepro-

384cessing methods were developed, implemented and

385tested. The full details of these are given in Baltsavias

386et al. (2001) and Figs. 5 and 6 illustrate the impact of

387the adopted image processing approaches. The first

388method consisted of an anisotropic Gaussian low-pass

389filtering to reduce noise and stripes in the scan direc-

390tion (which for nonstereo images are almost vertical),

391coupled with application of a Wallis filter for contrast

392enhancement (Fig. 5, middle). The filter anisotropy

393was aimed at filtering more along the image lines in

394order to reduce vertical striping.

395In a second preprocessing step, after the low-pass

396filtering, an unbiased anisotropic diffusion was used to

397further reduce noise and sharpen edges (Fig. 6). The

398improvement of theWallis filter in shadow regions was

399not as much as previously achieved for aerial and other

400satellite imagery, possibly due to a low signal variation

401in these regions. Both these approaches were applied to

4028-bit data that were generated by a linear compression

403of the original 11-bit imagery.

404Later, a third, improved approach was adopted.

405Two adaptive local filters were first developed. They

406reduced noise, while sharpening edges and preserving

407even fine detail such as one-pixel wide lines, corners

408and end points of lines (compare edge sharpness in the

409lower two images of Fig. 6). Optionally, salt-and-


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410 pepper noise can be eliminated to a certain extent. The

411 effect of the two local filters is generally quite similar,

412 although they use a different size and number of

413 masks, and one employs a fuzzy method. They require

414 as input an estimate of the noise, which may be

415 known or determined by the methods mentioned

416 above. Using this approach, the noise was reduced

417 to 30% of that exhibited by the original images. Next,

418 a new version of the Wallis filter is applied and this

419 estimates automatically some of the filter parameters.

420 Finally, a reduction to 8-bit imagery by histogram

421 equalisation is performed (Figs. 5 and 6), the histo-

422 gram equalisation being iterative so as to occupy all

423 eight bits with similar frequencies.

424 For the Ikonos imagery, processing in 11-bit led to

425 slightly better 8-bit images than the first two prepro-

426 cessing methods, even though the 11-bit data was

427 effectively only 8–9 bits. The histogram equalisation,

428 which is optimal for general computer processing but

429 may lead to strong bright and dark regions for visual

430 interpretation (see right image in Fig. 5), could be

431 replaced through an optimisation of a selected target

432 grey level range or by an alternative reduction of

433 Gaussian type.

434 4. Metric quality

435

436 4.1. Accuracy potential

437 The recovery of 3D cartographic information from

438 satellite line scanner imagery has been the subject of

439 photogrammetric investigation for the last decade and

440 a half. Mathematical models have been formulated to

441support bundle adjustment of cross-track SPOT and

442IRS-1C/D imagery, as well as MOMS-02 three-line

443imagery which is an along-track configuration with

444similarities to the Ikonos coverage of the Melbourne

445testfield. Fully rigorous mathematical models for

446orientation and triangulation, which have in turn

447implied provision of sensor calibration data and, to a

448degree, prior information on the satellite orbit and

449sensor attitude data, have been a prerequisite for these

450developments. In summary, it could be said that under

451ideal conditions of high-quality image mensuration

452and ground control/checkpoints, coupled with favour-

453able imaging geometry (e.g. B/H>0.8) and provision

454of sensor calibration data, 3D point positioning accu-

455racy corresponding to 0.3 of the pixel footprint is

456possible (Ebner et al., 1996) with medium-resolution

457stereo satellite imagery. However, accuracies of bet-

458ween 0.5 and 2 pixels are more commonly encountered

459in practical tests.

460Through a simple extrapolation of these findings to

4611-m resolution imagery, we might anticipate that under

462similar operational constraints submetre 3D position-

463ing accuracy should be obtainable from stereo Ikonos

464panchromatic and pan-sharpened image data. Con-

465sider, for example, a stereo Ikonos configuration sim-

466ilar to that covering the Melbourne testfield. Under the

467assumption of an image measurement accuracy of 0.4

468pixel (rXY =5 lm), the optimal 3D point positioning

469precision to be anticipated for the stereo configuration

470is rXY =0.3 m (planimetry) and rZ=0.7 m (height). If

471this stereo geometry is extended to three along-track

472images (addition of a nadir-looking image), again as

473we have for the Melbourne testfield, the 3D position-

474ing precision in Z remains unchanged (see also Ebner

Fig. 5. Original image (left), after first preprocessing method (middle) and third preprocessing method (right).


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475 et al., 1992), whereas the planimetric precision is

476 improved to rXY =0.25 m, or 1/4 of the ground sample

477 distance.

478

479 4.2. Sensor orientation models

480 At the present time, however, the option of apply-

481 ing rigorous, collinearity-based image orientation and

482 triangulation models to Ikonos data is effectively

483 denied to the end-user of this imagery, since the

484necessary camera model (calibration data) and precise

485ephemeris data for the satellite are withheld as propri-

486etary information. Alternative orientation/triangula-

487tion models are required, with the rational function

488model being currently supported (Grodecki, 2001;

489Grodecki and Dial, 2001; Yang, 2000; Hu and Tao,

4902001). The 80 RPCs per image for the transformation

491from offset and scaled latitude, longitude and height

492to similarly normalised image line and sample coor-

493dinates are computed from a 3D object point grid

Fig. 6. Top: original image (left) and after Wallis filtering to indicate noise content, especially in homogeneous areas (right). Bottom:

preprocessing by the second (left) and third (right) methods.


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494 and the corresponding image coordinates. The object

495 points are in turn derived via the rigorous sensor model

496 and known exterior orientation, which may be refined

497 by bundle adjustment (Grodecki and Dial, 2001). The

498 RPCs can be expected to yield absolute accuracies in

499 subsequent 3D point positioning that are consistent

500 with the specifications for the particular class of Ikonos

501 product, i.e. Geo, Precision, etc. RPCs for the Geo

502 product, which are expected to produce an RMS

503 positioning accuracy of about 25 m, are derived solely

504 from satellite ephemeris and attitude data without the

505 additional aid of ground control. As will be demon-

506 strated, there is considerable scope for enhancing the

507 absolute accuracy of XYZ data obtained via RPCs

508 through post transformation of the object point coor-

509 dinates to control points in order to remove exterior

510 orientation biases. This transformation may in the

511 simplest case be a coordinate translation, or it may

512 comprise a 3D translation and rotation.

513 Other polynomial models, either simpler or more

514 complicated than the RPCs, can be estimated with

515 similar methods using a strict sensor model. Options

516 include nonrational polynomials (e.g. Kratky, 1989;

517 Baltsavias and Stallmann, 1992), interpolation techni-

518 ques using known object-to-image transformations for

519 regular object or image grids (OGC, 1999) or the Uni-

520 versal Image Geometry Model (OGC, 1999), which

521 is an extension of the RPC model. However, Space

522 Imaging is not currently offering such modelling

523 options. Moreover, users cannot readily employ these

524 alternative models since the Ikonos sensor model has

525 not been released. A discussion of the use of rational

526 functions for photogrammetric restitution is given in

527 Dowman and Dolloff (2000). RPCs can be estimated

528 by using GCPs and not through a reparameterisation of

529 the rigorous sensor model, and this option is provided

530 by some commercial photogrammetric systems. Other

531 nonrational polynomial models (e.g. Kratky, 1989;

532 Papapanagiotu and Hatzopoulos, 2000) can be sim-

533 ilarly estimated. However, estimation of such poly-

534 nomial coefficients using GCPs needs many control

535 points that cover the entire planimetric and height

536 range of the scene and this can be very difficult to

537 achieve in practise, as well as being very costly. It can

538 also lead to severe extrapolation errors and possible

539 undulations between GCPs, especially with higher

540 degree polynomials. As will be illustrated, the use of

541 so-called terrain-dependent RPCs (Hu and Tao, 2001)

542is likely to be unwarranted for Ikonos imagery since

543alternative, straightforward models that yield high

544accuracy are available.

545Beyond rational functions, there are further mod-

546els displaying a favourable level of sensor independ-

547ence, which can be employed in order to overcome

548the absolute accuracy limitations of RPCs. Potential

549approaches that take account to varying degrees of the

550mixed projective/parallel projection of linear CCD

551sensors include the direct linear transformation or

552DLT (El-Madanili and Novak, 1996; Savopol and

553Armenakis, 1998), an extended DLT form (Wang,

5541999; Fraser et al., 2001, in press), or the use of

555piece-wise DLT functions (Yang, 2001). A second

556option is affine projection (Okamoto et al., 1999;

557Fraser, 2000). The former is fundamentally a projec-

558tive model, whereas the latter is better suited to

559Ikonos’ nominally parallel along-scan projection.

560The very small field of view of the sensor of only

5610.93j leads to the overall projection approaching

562skew parallel in CCD-line direction (in scan direction

563it is parallel), especially over smaller subscene areas.

564As it happens, both these models may be thought of as

565linear rational functions. In the case of the affine

566model, a perspective-to-affine transformation of the

567image may first be warranted in stereo scenes con-

568taining moderate to high relief variation, though this is

569more the case with sensors having a larger field of

570view than Ikonos, e.g. SPOT. The object-to-image

571transformation functions for the DLT and affine mod-

572els are given, respectively, as:

x ¼ ðL1X þ L2Y þ L3Z þ L4Þ=ðL9X þ L10Y573574þL11Z þ 1Þ575576

y ¼ ðL5X þ L6Y þ L7Z þ L8Þ=ðL9X þ L10Y577578þL11Z þ 1Þ ð1Þ

579580and

x ¼ A1X þ A2Y þ A3Z þ A4

581582

y ¼ A5X þ A6Y þ A7Z þ A8 ð2Þ

583584where x and y are image space coordinates in the scan

585and line-CCD direction, respectively, and X, Yand Z are

586Cartesian object space coordinates. For both the two-


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587 and three-image orientation/triangulation computa-

588 tions with the DLT and affine models, all parameters

589 can be determined simultaneously. Further details are

590 provided in Fraser et al. (2001). Additional parameters

591 can be included in the model of Eq. (1) to account for

592 nonlinearities in the DLT formulation for line scanner

593 imagery. In the present investigation, however, it was

594 found that such an extended form, as suggested by

595 Wang (1999), did not yield any measurable accuracy

596 improvement over the standard DLT model.

597 A variant to the 3D affine model, here called relief-

598 corrected affine transformation, is similar but not

599 identical to the affine projection of Eq. (2). It explic-

600 itly takes into account that Geo imagery is rectified to

601 an inflated ellipsoid and map projected without terrain

602 relief corrections. To relate object information to such

603 imagery, the object coordinates can first be corrected

604 by relief displacements using a flat or curved Earth

605 model. A simple affine transformation can then relate

606 object and image space without the need to apply any

607 image transformation (from central perspective to

608 affine to account for terrain relief). In fact, since the

609 relative accuracy of Geo images is at the submetre

610 level, as will be shown here, the major correction

611 needed is a shift between the two spaces. The two

612 scale and shear terms are required to model smaller

613 deviations between the two spaces. It was found in

614 tests involving various Geo images that in affine

615 transformation between the two spaces, with GCPs

616 in different projection systems, the two scale terms

617 deviated from unity in the fourth or fifth decimal only.

618 Also, the rotation was of the order of 1–2j (except of

619 course for stereo images which are rotated) and non-

620 orthogonality of the axes ranged from 0.003 to 0.03j.621 The projection of the GCPs onto a reference plane

622 leads to corrections of the planimetric X and Y

623 coordinates according to Eq. (3):

DX ¼ �ðZ � ZoÞsina=tane624625

DY ¼ �ðZ � ZoÞcosa=tane ð3Þ

626627 where Z is the height of a given ground point, Z0 the

628 height of the reference plane, a the sensor azimuth, e

629 the sensor elevation and DX and DY the planimetric

630 displacements of the object point due to projection

631 onto the reference plane. The azimuth and elevation

632 values can be obtained from the metadata file deliv-

633 ered with the Geo images.

634The choice of the reference height plane can be

635arbitrary and the distribution of the GCPs is not

636critical so long as the affine parameters can be

637determined reliably. This can be achieved if the range

638of their coordinates in two nominally orthogonal

639directions covers approximately a third or more of

640the area imaged. In the reported implementation, the

641projection to the reference plane and the affine trans-

642formation are combined in a single operation involv-

643ing eight parameters, just as in Eq. (2), though only

644six coefficients are independent since

A3 ¼ �ðA1 sinaþ A2 cos aÞ=tane645646

A7 ¼ �ðA5 sinaþ A6 cosaÞ=tane ð4Þ647648

649The relief-corrected affine transformation can be

650used not only for object-to-image transformation and

651orthoimage generation (Kersten et al., 2000; Baltsa-

652vias et al., 2001), but also for 3D spatial intersection

653and automatic DSM generation using two or more

654images, including application to constrained image

655matching and epipolar resampling. The effective re-

656duction in the number of parameters from the eight of

657Eq. (2) to the six of Eq. (4) is possible because the

658sensor elevation and azimuth are known. Thus, advan-

659tages of the approach are the need for fewer GCPs and

660the limited influence of their distribution. In addition,

661if GCPs along a water body are selected, the height

662need be known for only one GCP.

663For the DLT and two affine models, the parameters

664(including sensor azimuth and elevation that vary very

665little within a scene) are considered constant for the full

666scene. This is appropriate for the normal scanning

667mode of Ikonos and for the three images evaluated,

668which were all north-to-south scans. The normal for-

669ward scanning mode of Ikonos is in N–S strips, termed

670containers by SI, where the sensor elevation and azi-

671muth, although given for every scene of the strip, are

672constant within each strip. Multiple neighbouring strips

673can be acquired during one pass, each strip having

674different azimuth and possibly elevation. This N–S

675scan is typical for Ikonos images and should not be

676confused with the flight or track direction which differ.

677Ikonos can also scan in the reverse mode, i.e. from S to

678N, by continuously varying the elevation (but with

679constant azimuth). Although the satellite is agile, it is


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680 unknown to the authors whether Ikonos can image one

681 scene while also varying the azimuth.

682 The scanning in directions other than N–S or S–N,

683 e.g. in W–E, would require a change of both elevation

684 and azimuth. Examination of many images in the

685 Carterra Online Catalogue of SI USA, including

686 emergency cases like the Manhattan images of 12

687 September 2001 and southeastern Hainan images of

688 April 2001 has, however, shown that Ikonos images

689 are even in these cases in a N–S direction. When an

690 area (strip) needs to be covered in directions other

691 than N–S or W–E, this can be achieved by rotating

692 the satellite and the sensor with respect to the N–S

693 direction (effectively applying a kappa rotation), see,

694 e.g. Manhattan images of 12 September 2001. Thus,

695 any area can be imaged while still keeping azimuth

696 and elevation constant within each strip. Even if

697 imaging with continuous change of elevation and

698 azimuth would be possible, it does not necessarily

699 lead to coverage of a larger area, requires power

700 consumption for the satellite manouvering, can lead

701 to image quality deterioration though oversampling or

702 undersampling if imaging and satellite manouvering

703 are not perfectly synchronised, and leads to varying

704 elevation and, thus, also to different image quality

705 within a scene. It is also interesting to note that based

706 on SI investigations (Cook et al., 2001), the reverse

707 scan mode showed much poorer planimetric accuracy

708 than the forward mode for Level 4a standard orthor-

709 ectified products (without use of GCPs).

710 Based on the Carterra Online data, our assumption

711 of constant elevation and azimuth seems to be fulfilled

712 for each strip. Furthermore, even if this were not the

713 case, we could still process long strips with our linear

714 models, as long as for each scene (typically 11 by 11

715 km) elevation and azimuth are constant. This would

716 require use of a different set of model parameters for

717 each scene. If, however, we keep one set of model

718 parameters for all scenes of a strip, the DLT and affine

719 models, being linear approximations to higher-order

720 rational functions, can be expected to show limitations,

721 especially as the area covered by the Ikonos strip

722 becomes long and, thus, the constant angle model

723 may need extension via time-dependent parameters

724 (e.g. Okamoto et al., 1999). Similarly, if elevation

725 and azimuth vary within one scene, accurate orientation

726 may require higher-order models. As will be illustrated,

727 however, in the 50-km2 Melbourne testfield all three

728models yielded submetre 3D positioning accuracy

729when applied with as few as three to six GCPs.

730

7314.3. Planimetric positioning

732In many respects, a prerequisite to the application of

733approaches such as affine projection and the DLT is

734that the imagery itself is of high metric integrity. As a

735means of both ascertaining the planimetric positioning

736accuracy of 1-m Ikonos imagery and of evaluating

737‘sensor linearity,’ a number of 2D transformations

738(similarity, affine and projective) from image to object

739space were performed. The aim was to examine the

740accuracy in XY coordinates resulting from mapping the

741image data to height-corrected planes using various

742GCP configurations. The results of this 2D accuracy

743analysis, which are reported in more detail in Hanley

744and Fraser (2001), were very encouraging. For three

745configurations of six GCPs and 20–25 independent

746checkpoints, RMS positioning accuracies of 0.3–0.5

747m were obtained. When a best-fit mapping was carried

748out using all 32 GCPs, the XY discrepancies displayed

749RMS values ranging from 0.27 to 0.38 m, i.e. from

750about 0.3 to 0.4 pixels.

751Furthermore, the GCPs without projection to a

752plane were transformed with RPCs to image space

753and compared to the measured image coordinates

754(Baltsavias et al., 2001). While the absolute errors

755had a high bias of 29 and 16 pixels for the x and y

756pixel coordinates, respectively, the standard deviations

757were 0.4–0.6 pixels, showing a high relative accuracy

758for the RPCs. Although the 2D affine model with

759height-corrected GCPS is effectively equivalent to the

760explicit relief-corrected affine transformation, the lat-

761ter was applied with a reference height of sea-level

762and transformations beyond 2D affine, namely bilin-

763ear, quadratic and biquadratic were employed using

764all available GCPs. The XY accuracy results were

765practically identical for all transformations being

7660.41/0.46/0.36 and 0.60/0.60/0.45 pixels, respectively,

767for the ellipse fit and matching datasets for the left/

768right/nadir images.

769The nadir image gave better results probably due to

770the better image quality and more accurate relief

771correction, since height errors influence the relief

772displacement by some five times less than in the stereo

773images due to the higher sensor elevation. One feature

774of note with the relief-corrected affine model was that a


UNCORRECTED PROOF

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775 configuration of three GCPS gave similar accuracies to

776 six or eight control points. This provided a confirma-

777 tion that the most important factor is GCP quality rather

778 than quantity. The results with three GCPs, which had

779 good but not optimal distribution, were worse than the

780 32-point GCP case by only 14% and 24% for the stereo

781 and nadir images, respectively. This illustrated that

782 three highly accurate GCPs with a reasonable distribu-

783 tion can yield subpixel accuracy in the transformation

784 from object to image space.

785 In the absence of any camera model information,

786 it was felt that the reported 2D analysis gave a

787 strong indication of the metric integrity of Ikonos Geo

788 imagery, which augured well for the use of linear and

789 DLT models in 3D point positioning. Moreover, the

790 tests demonstrated that in the presence of good quality

791 control and DTM data (or moderately flat terrain) a

792 single Ikonos image can readily yield an XY position-

793 ing accuracy of 0.5 pixel or better, whether it be for

794 point positioning or ortho-image generation (e.g. Balt-

795 savias et al., 2001).

796

797 4.4. 3D positioning of roundabouts

798 4.4.1. Rational functions

799 As a first step in analysing the 3D spatial intersec-

800 tion accuracy of Ikonos stereo images, 3D coordinates

801 for the GPS-surveyed roundabouts were computed

802 using the supplied RPCs, for the cases of both man-

803 ually measured image coordinates (via ellipse fitting)

804 and least-squares template matching. The 3D spatial

805 intersection is computed by least-squares adjustment

806 with initial approximations derived using just the

807 linear terms of the RPCs (see details in Baltsavias et

808 al., 2001; Fraser et al., in press). The resulting RMS

809 discrepancy in XYZ ground point coordinates averaged

810 8, 32 and 2 m in X, Y and Z, respectively, which was

811 largely consistent with Geo specifications. The pres-

812 ence of a positioning bias close to the RMS values was

813 clearly apparent, however, and a simple coordinate

814 translation using six control points reduced the RMS

815 discrepancy at remaining checkpoints to 0.4–0.6 m in

816 planimetry and 0.6–0.8 m in height. A 2D translation

817 (bias removal) in image space using six GCPs and

818 subsequent 3D spatial intersection using the RPCs

819 gave similar results to the translation in object space.

820 As shown in Fraser et al. (in press), it is also possible to

821 recover image coordinate biases via an additional

822parameter approach, which is particularly well suited

823to multiimage configurations where the bias character-

824istics for each image are different.

825The results obtained compare very favourably to the

8261–2-m planimetric and height accuracy reported for

827Ikonos Precision imagery in Grodecki and Dial (2001)

828and surpass those from investigations employing Geo

829imagery that have utilised more, but less precise,

830ground control points and RPCs (Hu and Tao, 2001)

831or proprietary sensor models (Toutin et al., 2001). In

832regard to a comparison of results obtained from least-

833squares matching and ellipse fitting, the image coor-

834dinates obtained from matching were slightly worse

835due to some unmodelled disturbances of the round-

836about perimeters, but they yielded more consistent

837accuracy in height determination.

838Computations of 3D spatial intersection for the 32

839roundabouts within the Melbourne Ikonos testfield,

840which are further discussed in Baltsavias et al. (2001)

841and Fraser et al. (2001), confirmed that high relative

842accuracies are attainable with Geo RPCs in spite of

843their rather modest absolute accuracy specification.

844Provision of only one or two GCPs facilitated removal

845of positioning biases, which were much larger in

846planimetry than height, to yield 1–2-m level 3D

847positioning accuracy, whereas subpixel accuracies

848were achieved with additional GCPs. The results were

849insensitive in the selection of the GCPs used to remove

850the bias, as long as they were fairly well distributed. It

851should be noted though that RPCs have until recently

852been provided by Space Imaging as a standard product

853for stereo imagery only. Since July 2001, RPCs have

854been made available with the Geo OrthoKit product,

855but this is considerably more expensive than the stand-

856ard Geo product (OrthoKit is currently offered by

857Space Imaging at a 61–85% surcharge).

858

8594.4.2. Affine projection and DLT models

860With the affine projection and DLT orientation/

861triangulation models it is a straightforward matter to

862extend the analysis of stereo Ikonos imagery to an

863evaluation of three-image configurations. A series of

864bundle1 adjustments were computed using both the

865DLT (Eq. (1)) and the affine projection model (Eq.

1 Strictly speaking, the term bundle should be used for a two-

dimensional sensor only, but here we use it also for linear CCDs.


UNCORRECTED PROOF

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866 (2)). A full account of the results is beyond the scope

867 of the present paper and readers are referred to Fraser

868 et al. (2001, in press) for further details. The results

869 can, however, be summarised as follows: For control

870 configurations of four, six and eight GCPs and 20–25

871 checkpoints, the affine model produced RMS 3D

872 positioning accuracies of 0.3–0.5 m in XY and 0.5–

873 0.7 m in Z, for both stereo and three-image geo-

874 metries. Accuracy deteriorated only slightly with

875 decreasing numbers of control points, while use of

876 three instead of two images improved the results,

877 especially in height. For the same number of control

878 points, the affine model yielded slightly better results

879 than the RPC-based spatial intersection. For the same

880 GCP sets, the DLT yielded slightly lower 3D position-

881 ing accuracy of 0.3–0.6 m in XY and 0.5–0.9 m in Z,

882 and it was found to exhibit stability problems for

883 certain GCP configurations. The fact that first-order

884 models can readily yield subpixel positioning accu-

885 racy is very encouraging for both metre-level city

886 modelling applications and for GIS mapping for local

887 government planning and management, which requires

888 nominal 1r precision of 1.5–2 m (e.g. Davis and

889 Wang, 2001).

890

891 4.4.3. Relief-corrected affine transformation

892 We consider the relief-corrected affine model sep-

893 arately since this method did not employ simultane-

894 ous computation of transformation parameters and

895 object point coordinates. Instead, a two-stage 3D spa-

896 tial resection/intersection approach was followed. The

897 affine transformations that were computed for each

898 image independently (Section 4.2) utilised six param-

899 eters as the parameters A3 and A7 were computed by

900 Eq. (4). The final eight affine parameters (Eq. (2))

901 were then used with the same algorithm for 3D spatial

902 intersection that was employed for the RPCs (see

903 Section 4.4.1), though without bias removal. The

904 RMS errors in planimetry and height, using the ellipse

905 fit dataset and two images were 0.43 and 0.76 m for

906 three GCPs, and 0.45 and 0.71 m for eight GCPs. This

907 illustrates the small influence of the number of GCPs

908 upon achievable accuracy. The results using all three

909 images were better, being 0.32 and 0.57 m, and 0.33

910 and 0.60 m for 3- and 8-point GCP configurations,

911 respectively.

912 In all cases, planimetric accuracy was in the 0.3–

913 0.5-m range and height accuracy in the 0.6–0.8-m

914range. The results are very similar to those of the

915‘standard’ affine projection, however, some differ-

916ences exist. Affine projection (Eq. (2)) estimates eight

917parameters using the measured XYZ coordinates of

918GCPs along with bundle adjustment, whereas the

919relief-corrected approach involves only six independ-

920ent parameters for each image and it employs cor-

921rected planimetric coordinates of GCPs (Eq. (3)). The

922latter approach needs only three GCPs and requires no

923image pretransformation from central perspective to

924affine projection. The use of bundle adjustment for

925determining the six relief-corrected affine parameters

926could be favourably used but it has not as yet been

927implemented.

9285. Building extraction

929Whereas the foregoing 3D point positioning eval-

930uation was centred upon high-quality targets, accurate

931positioning of building features, generally corners and

932edges, involves not only metric factors but issues of

933image resolution and feature identification. The ap-

934proach adopted in this investigation for ascertaining

935the metric quality of building extraction in stereo

936Ikonos imagery again involved independent checks

937of photogrammetrically triangulated, distinct points

938against their GPS-surveyed positions. As mentioned,

93919 image-identifiable roof corners were precisely

940surveyed to serve as accuracy checkpoints in the

941building extraction phase.

942Within the stereo spatial intersection of roof cor-

943ners, the RPCs produced RMS accuracies of 0.7 m in

944planimetry and 0.9 m in height after removal of the

945bias in object space using six GCPs. Corresponding

946accuracy estimates resulting from the 19 checkpoints

947for the affine projection and DLT models with six

948GCPs were 0.6 and 0.7 m in planimetry, and 0.8 and

9491.0 m in height, respectively. Spatial intersection of

950the threefold image coverage produced results that

951were not significantly different than those for the

952stereo restitution. Whereas in practice it is unlikely

953that a three-image coverage would be employed for

954building extraction, provision of the near-nadir image

955can prove very useful for subsequent ortho-image

956generation over built-up urban areas.

957In order to provide a qualitative and more exten-

958sive quantitative assessment of the potential of stereo


UNCORRECTED PROOF

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959 Ikonos imagery for the generation of building models,

960 the University of Melbourne campus was measured

961 manually in stereo using an in-house developed soft-

962 ware tool for the Ikonos stereo images, and an

963 analytical plotter for the 1:15,000 colour aerial

964 imagery. The resulting plots of extracted building

965 features are shown in Fig. 7. The manual measure-

966 ments of roof corners and points of detail were

967 topologically structured automatically using the soft-

968 ware package CC-Modeler (Gruen and Wang, 1998)

969 and also visualised as indicated by Fig. 8.

970 A comparison of the two models in Fig. 7, one

971 from aerial photography and the other from Ikonos 1-

972 m imagery, reveals the following regarding the Ikonos

973stereo feature extraction: About 15% of the buildings

974measured in the aerial images could not be modelled

975and it is interesting to note that this figure fits well to

976the findings of Ridley et al. (1997). Also, a number of

977both small and large buildings could not be identified

978and measured, though some new buildings could,

979however, be reconstructed, even if small. Finally, as

980indicated in Fig. 9, buildings could often only be

981generalised with a simplified roof structure and var-

982iations to their form and size. Measurement and

983interpretation in stereo proved to be a considerable

984advantage, and we expect that colour imagery would

985also have been very advantageous were it available.

986Other factors influencing the feature extraction proc-

Fig. 7. Buildings of University of Melbourne campus reconstructed from 1:15,000 aerial images (left) and stereo Ikonos imagery (right). To

simplify visualisation, first points and first lines have been omitted in the left figure.


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987 ess are shadows, occlusions, edge definition (related

988 also to noise and artifacts), saturation of bright surfa-

989 ces, sun and sensor elevation and azimuth, and atmos-

990 pheric conditions. The 1-m resolution of Ikonos also

991 leads to certain interpretation restrictions. This inves-

992 tigation was aimed in part at quantifying the extent of

993 such limitations. However, additional tests with differ-

994 ent Ikonos stereo imagery are needed in order to draw

995 more comprehensive conclusions.

9966. Conclusions

997The reported investigation has shown that Geo

998stereo and three-image configurations have the poten-

999tial to yield submetre accuracy in both planimetry and

1000height without a requirement for provision of the

1001Ikonos camera model and orbit ephemeris data. More-

1002over, this high level of accuracy is obtained with

1003straightforward linear and DLT orientation models at

Fig. 8. Visualisation from stereo Ikonos images of extracted buildings and trees of University of Melbourne campus.

Fig. 9. Building with complicated roof structure as extracted from stereo aerial (left) and Ikonos images (right).


UNCORRECTED PROOF

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1004 the modest cost of providing a small number of, say,

1005 three to six GCPs with dm accuracy. As regards

1006 alternative sensor orientation models, it has been

1007 shown that RPCs with bias removal, affine projection,

1008 the DLT and relief-corrected affine transformation are

1009 all suited to high-accuracy 3D positioning, with accu-

1010 racy for two and three images reaching 0.3–0.6 m in

1011 planimetry and 0.5–0.9 m in height. In fact, the two

1012 affine model approaches yielded the most accurate

1013 results within the 7�7-km Melbourne testfield, show-

1014 ing that the Geo product (without RPCs) can be used

1015 for 3D point positioning and object extraction to a

1016 precision normally associated with only the most

1017 expensive Ikonos product, Precision Plus imagery.

1018 The results obtained are encouraging but must be

1019 weighed against some notable shortcomings of 1-m

1020 satellite imagery, especially for the reported case of

1021 building feature detection and identification. While

1022 the limitations of an image resolution of 1 m are well

1023 understood, it is more the variability of image quality

1024 from scene to scene that will limit application to

1025 building reconstruction and city modelling. The influ-

1026 ence of factors which are largely beyond the current

1027 control of the image user, such as date and time of

1028 image collection, specification of favourable sun

1029 angles and atmospheric conditions, etc. will likely

1030 generate difficulties if one is seeking to exploit the

1031 imagery to its full potential. Problems were encoun-

1032 tered in this investigation, not only in identifying all

1033 buildings of a certain size, but also in accurately

1034 reconstructing their form without excessive general-

1035 isation. The shortcomings in radiometric quality,

1036 although not of major concern in the present study,

1037 also give rise to accuracy and interpretability concerns

1038 in both manual and computer mensuration. However,

1039 the issues of radiometric inhomogeneity, both within

1040 and between Ikonos scenes, are unlikely to be affected

1041 by the level of image product purchased and this

1042 investigation has demonstrated that very high metric

1043 performance is achievable with the least expensive

1044 product offering, namely Geo imagery.

1045 Acknowledgements

1046 The computational and general research support of

1047 Harry Hanley and Takeshi Yamakawa at the Univer-

1048 sity of Melbourne, and Maria Pateraki and Li Zhang at

1049ETH Zurich, in this collaborative project is gratefully

1050acknowledged. The Ikonos image of Lucerne was

1051kindly provided by the National Point of Contact,

1052Swiss Federal Office of Topography, while the Ikonos

1053image of Nisyros was made available by Prof. E.

1054Lagios, University of Athens, Greece, within the EU

1055project Geowarn.

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