Geometric Integration of Aerial and High-Resolution Satellite Imagery and Application in Shoreline Mapping

Transcription

1 Marine Geodesy, 31: , 2008 Copyright Taylor & Francis Group, LLC ISSN: print / X online DOI: / Geometric Integration of Aerial and High-Resolution Satellite Imagery and Application in Shoreline Mapping RONGXING LI, 1 SAGAR DESHPANDE, 1 XUTONG NIU, 2 FENG ZHOU, 1 KAICHANG DI, 1 AND BO WU 1 Introduction 1 Mapping and GIS Laboratory, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Ohio, USA 2 Department of Math, Physics, Computer Science and Geomatics, Troy University, Troy, Alabama, USA This paper investigates the geopositioning accuracy achievable from integrating IKONOS and QuickBird satellite stereo image pairs with aerial images acquired over a region at Tampa Bay, Florida. The results showed that the accuracy is related to a few factors of imaging geometry. For example, the geopositioning accuracy of a stereo pair of IKONOS or QuickBird images can be improved by integrating a set of aerial images, even just a single aerial image or a stereo pair of aerial images. Shorelines derived from the IKONOS and QuickBird stereo images, particularly the vertical positions, are compared with the corresponding observations of water-penetrating LiDAR and water gauge stations and proved that differences are within the limit of the geopositioning uncertainty of the satellite images. Keywords High-resolution satellite images, aerial images, geometric integration, geopositioning accuracy, shoreline The launch of civilian high-resolution imaging satellites, such as IKONOS and QuickBird, has initiated a new era of Earth observation and digital mapping (Li 1998). During the past several years, high-resolution satellite imagery (HRSI) has been widely used in digital topographic mapping and surveying. HRSI is very attractive, having the advantages of sub-meter to meter level of resolution, short revisit time, and adaptable stereo imaging capabilities. These advantages have made HRSI popular for mapping tasks, as well as for coastal applications (Li 1998; Li et al. 2007; Fraser 1999; Tao et al. 2004). On the other hand, aerial images have been widely used in high accuracy topographic mapping because of their centimeter level of resolution and maturity of the technology. As more image data from a variety of sources has become easily available, it is highly desirable to study the geopositioning accuracy that could be obtained by different combinations of imagery. Received 19 May 2008; accepted 9 June Address correspondence to Rongxing Li, Mapping and GIS Laboratory, Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH li.282@osu.edu 143

2 144 R. Li et al. Combining less costly satellite imagery with traditional aerial images could provide both practical and theoretical solutions for selecting image sources for accurate 3-D mapping. This paper investigates accuracy from the integration of IKONOS, QuickBird, and aerial images using the data collected at Tampa Bay, Florida. The 3-D geopositioning accuracies for various integration schemes were obtained and analyzed. The integration results were used to produce 3-D shorelines from the satellite images, and the results were compared with the corresponding observations of water-penetrating LiDAR and water gauge stations. Based on the results obtained, discussions and conclusions are presented. Brief Review of Related Previous Work For HRSI, many sensor models have been presented to describe the geometrical relationships between the object space and the image space, including the Rigorous Physical Model (RPM) and Rational Function Model (RFM) (Fraser 1999; Tao and Hu 2001; Di et al. 2003a; Wang et al. 2005). The RPM for various satellite image products may be unavailable. Instead, vendors may just provide RFMs in the form of rational polynomial coefficients (RPCs). These RFMs describe the orientation information of the high-resolution imaging systems in an implicit way (Di et al. 2003a; Tao and Hu 2001). Advantages of the RFM include computational simplicity, implicitness of physical setting of the imaging system, and independence from sensor types. The RFM performs a transformation between an image point (i, j) and its corresponding ground space point (X, Y, Z) through the ratio of two polynomials i = P 1 (X, Y, Z)/P 2 (X, Y, Z) j = P 3 (X, Y, Z)/P 4 (X, Y, Z) where the P i (i = 1, 2, 3, and 4) is a third-order, 20-term polynomial. As a generalized sensor model, the RFM uses the above ratios of polynomials to represent the relationship between the image coordinates and the object coordinates, which have been traditionally represented by the collinearity equations in photogrammetry (Wolf and Dewitt 2000; Di et al. 2003a). Usually, the RPCs in Eq. (1) are not computed using ground control points (GCPs). Instead, virtual control points (VCPs) are created based on the full extent of the image and the range of elevation variation. The entire range of elevation variation is sliced into several layers. Then, the RPCs are calculated by a least-squares adjustment with these VCPs (Di et al. 2003a; Tao and Hu 2001). Some of the bias inherent in the RPCs may not be correctable and will be reflected in the achieved geopositioning accuracy. A systematic error of 6 m was reported by Li et al. (2003) between the RPC-derived coordinates and the ground truth. A similar result was reported by Fraser and Hanley (2003). Many researchers have focused on improving the accuracy of HRSI using GCPs. Dial (2000) estimated the stereo mapping accuracy of IKONOS products with GCPs as 1.32 m (RMSE) in the horizontal direction and 1.82 m (RMSE) in the vertical direction. Di et al. (2003b) used a 3-D affine transformation model to refine the RPC-derived ground coordinates for IKONOS images and achieved accuracies of better than 1.5 m in planimetry and 1.6 m in height. Wang et al. (2005) compared the results of different methods with corrections in both image space and object space to improve IKONOS stereo geopositioning accuracy. They looked at different transformation models with different GCP distributions, and found that the affine transformation can produce better accuracies when used with (1)

3 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 145 Table 1 Parameters of IKONOS and QuickBird stereo satellite imagery QuickBird IKONOS Forward Rear Forward Rear Acquisition date Acquisition time 15:58:08 15:59:17 16:17:17 16:18:08 (GMT) Image resolution m m 1 m 1 m Image size (row column) Collection azimuth (θ) Collection elevation (α) four to six evenly distributed GCPs. Similar results were found by Niu et al. (2004) in research on geopositioning accuracy using QuickBird stereo images where m level of accuracy was obtained. Accuracy better than 3 m (RMSE) in the X and Y directions for QuickBird Basic images using limited ground control was reported by Robertson (2003). Noguchi et al. (2004) obtained an accuracy of 0.6 m in planimetry and 0.5 m in height while investigating the geopositioning accuracy of QuickBird stereo images. In Li et al. (2007), satellite images from different satellite sensors (IKONOS and QuickBird satellite stereo images) were integrated to study the attainable geopositioning accuracy. The relationship between the satellite-borne pointing geometry and the attainable ground accuracy was examined. This research demonstrated that the integration of IKONOS and QuickBird images is feasible and can improve the 3-D geopositioning accuracy using a proper combination of images. Based on their work, this paper further investigates the attainable 3-D geopositioning accuracy by incorporating aerial images with IKONOS and QuickBird imagery. The methodology adopted and the results obtained are discussed in the following sections. The IKONOS and QuickBird Images and Ground Control Data This research examines a block of 24 aerial images, a pair of IKONOS stereo reference product images, and a pair of QuickBird stereo basic product images of the southern part of Tampa Bay, Florida (Figure 1). Aerial images having a ground resolution of 0.25 m were acquired in February The QuickBird stereo pair was acquired in September 2003; the IKONOS stereo pair was acquired in July The QuickBird and aerial images cover a larger common area, in which the IKONOS stereo pair covers only half of the area. The parameters of the images are listed in Table 1. A 3-D illustration of the satellite positions, aerial image exposure centers, and their footprints are given in Figure 2. Eleven GPS points from a GPS campaign conducted in September 2005 were used in the present study (triangles and circles in Figure 1). Most of these GPS points were located at corners of concrete pavements such that they can be clearly identified and measured in the images. The points are evenly distributed in the study area in Figure 1. Referenced to the State Plane Coordinate System (Florida West), the maximum errors of these GPS points are m in the X direction (easting), m in the Y direction (northing), and m

4 146 R. Li et al. Figure 1. The study area in Tampa Bay, Florida showing the distribution of ground control points (GCPs) and check points (CKPs) as well as the footprints of the IKONOS, QuickBird, and aerial images. in the Z direction (vertical). A 3-D view of these ground points with respect to the satellite and aerial imaging geometry is illustrated in Figure 2. Geometric Integration of Satellite and Aerial Images Aerial Image Processing Figure 3 shows an overview of the steps adopted for the geometric integration of the satellite and aerial images. The aerial image processing started with the block bundle adjustment of the 24 aerial images using the 11 GPS control points was performed by using ERDAS Imagine software (Leica Photogrammetry Suite). Five of these GPS points were used as ground control points (GCPs) and the remaining six as check points (CKPs). The GCPs were employed to compute the exterior orientation (EO) parameters of each aerial image through the network consisting of all aerial images and linked by 85 tie points determined by the software system. The ground coordinates of the CKPs were compared to those computed from the measured image coordinates and the bundle adjusted EO parameters to assess the quality of the aerial bundle adjustment as RMSE of the CKP coordinates. The resulting RMSEs in the X, Y, and Z directions were 0.28 m, 0.26 m, and 0.33 m, respectively. They

5 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 147 Figure 2. Three-dimensional illustration of the aerial image exposure centers and footprints, the satellite imaging positions, and the positions of the ground control points. represent a high level of ground accuracy for the points that can be identified and measured in the aerial images. Their ground coordinate can be precisely computed using the bundle adjusted EO parameters. In fact, some tie points used in the bundle adjustment of the aerial images were then utilized as CKPs in the subsequent integration process. In the next step of aerial image processing, RPCs for the aerial images were computed which can then be employed in the integration of the aerial images and the satellite images. The RPCs of the aerial images were calculated using the coordinates of the virtual control points (VCPs) that were defined on several planes constructed in the Z direction where a set of 3-D grid points are defined. The image coordinates of the VCPs were determined by using the interior orientation (IO) and exterior orientation (EO) parameters (Di et al. 2003a). The EO parameters of the aerial images were obtained from the bundle block adjustment described previously. The end results of aerial image processing were the computed RPCs.

6 148 R. Li et al. Figure 3. Workflow of the geometric integration of satellite and aerial images. Satellite Image Processing In satellite image processing, the vendor-provided RPCs for the satellite images are usually computed without using GCPs. Therefore, there is a need to use local GCPs to correct the ground error (predominantly a shift) that exists in either IKONOS and/or QuickBird data. This is performed by using an affine transformation correction model (Wang et al. 2005) in the image space and four GCPs: i = a 0 + a 1 i + a 2 j j = b 0 + b 1 i + b 2 j where (i, j) and (i, j ) represent the measured and corrected image coordinates of a GCP. Measured image coordinates of GCPs can be obtained by manually identifying the location of GCPs on the image. Given 3-D coordinates of a GCP, the corrected image coordinates (i, j ) can be computed from the vendor-provided RPCs. The affine transformation parameters are represented by a 0, a 1, a 2, b 0, b 1, and b 2 and can thus be estimated by the four GCPs. This makes sure that for any point, if its measured image coordinates in a stereo pair are corrected by this affine transformation model, the refined image coordinates of the conjugate image points can be employed to triangulate the ground point with an improved ground accuracy. Consequently, the measured image coordinates of the CKPs in the satellite images were refined using the computed affine transformation parameters. Since the CKPs are utilized in assessment of the geometric integration study, their image coordinates in the aerial images and refined image coordinates in IKONOS and QuickBird satellite images, as well as the RPCs of these images, were made available for further analysis. (2) Geometric Integration of Satellite and Aerial Images We studied two types of configurations of geometric integration of satellite and aerial images. Type I includes one stereo pair of IKONOS images, one stereo pair of QuickBird

7 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 149 Figure 4. Distribution of GCPs and CKPs used in the integration of satellite and aerial images. The background is the forward-looking QuickBird image. images, and all 24 aerial images. Type II handles the same satellite images and one single aerial image (or one stereo pair of aerial images). Type I Integration. Four evenly distributed GPS control points (marked as a star and three squares in Figure 4) were used as GCPs for the QuickBird stereo pair. Similarly, four evenly distributed GPS control points (marked as a star and three triangles in Figure 4) were used as GCPs for the IKONOS stereo pair. Only one common GCP (marked as a star in Figure 4) was found between the IKONOS and QuickBird images due to the even distribution of control points over the different extent of IKONOS and QuickBird image pairs. Twenty-seven CKPs were found in the area common to all of the satellite and aerial images, and they were among the tie points used for an aerial bundle block adjustment. Their high accuracy ground coordinates can be applied to check the geopositioning accuracy of different integration combinations.

8 150 R. Li et al. Table 2 Geopositioning accuracy: integration of IKONOS and QuickBird satellite images with all aerial images 3-D Geopositioning Accuracy (RMSE: meters) ID Combination σ x σ y σ z 1 IKONOS (S) QuickBird (S) IKONOS (S) + QuickBird (S) Aerial images + IKONOS (S) Aerial images + QuickBird (S) Aerial images + IKONOS (S) QuickBird (S) 7 Aerial images Note: S stereo. With all the available images, satellite and aerial, either a stereo pair or a block of images (same or different kind) can be formed to determine ground positions of objects in the object space through RPCs and refined image point measurements. Seven different combinations of satellite and aerial images were considered to study the effect of the geometric integration (Table 2). The CKPs served as the points where the differences between the known coordinates and those computed from each combination were calculated and the RMSEs were derived. The RMSEs are compared to assess the integration quality in Table 2 and Figure 5. From the above experimental result, the following should be noted: 1. The geopositioning accuracy calculated for the QuickBird stereo images is better than that for the IKONOS pair; the magnitude of the differences seems to be in accordance with their resolutions. 2. There is a significant improvement in geopositioning accuracy when all the aerial images are combined with a single pair of IKONOS or QuickBird images, or both pairs, compared to the cases involving satellite images only. 3. The geopositioning accuracy of all aerial images is the highest. This may be attributed to the high-resolution (0.25 m) and a strong image network. That means that adding any lower resolute satellite images in this case does not help enhance the geopositioning capability. Type II Integration. In the Type II integration, only one aerial image (Image ID in Figure 1), or one pair of aerial images (Image IDs and in Figure 1) was integrated with two pairs of satellite images (IKONOS and/or QuickBird) instead of all the aerial images. The footprints of the two aerial images are also illustrated in Figure 2. Based on the results of the Type I integration, one of the objectives is to examine if the geopositioning accuracy can be improved by adding one single aerial image or a stereo pair of aerial images to the satellite images. Furthermore, an analysis of the impact of the imaging geometry of a stereo pair of the satellite images should be conducted in terms of its convergence angle formed by the two optical rays from the object point to

9 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 151 Figure 5. RMSE values obtained from seven configurations of IKONOS and QuickBird satellite images and all the aerial images. its corresponding image points. According to our previous research results using only one stereo pair, a weak imaging geometry may be established by a small convergence angle (Li et al. 2007; Niu et al. 2005). Ten CKPs in the area common to the aerial stereo pair and the satellite images were used for this analysis. The RMSEs for each combination were calculated by comparing the computed ground coordinates of the CKPs with those obtained from the bundle block adjustment. The results obtained are tabulated in Table 3 and graphically represented in Figure 6. Observing the above geometric integration results in Table 3 and Figure 6, the following should be noted: 1. The geopositioning accuracy of the satellite images, particularly in the cases of the IKONOS stereo pair and QuickBird stereo pair, is improved by adding one single aerial image or one pair of stereo aerial images. 2. In most cases, the accuracies of the X and Y coordinates are better than that of the Z coordinate, even when one single aerial image or one stereo pair of aerial images are added. This is consistent with the results published previously for cases dealing with satellite stereo images only (Li et al. 2007). 3. Compared with the integration with one single aerial image, the accuracy obtained from adding a stereo pair of aerial images to the satellite images is further increased in the Y and Z directions, especially the Y direction (Table 3 and Figure 6b). This improvement is due mainly to the formation of the stereo pair along the Y direction. 4. Small convergence angles of the stereo satellite image pairs appeared to create weaker imaging geometry and result in lower disparities and, subsequently, lower accuracies in the Z coordinate, particularly for Combination 1 in Table 3 (rear-looking IKONOS and QuickBird images). An exception is for Combination 2 where the minimum convergence angle of the two satellite images corresponds to the best overall accuracy. This should be interpreted as strengthening the imaging geometry by adding a single or a pair

10 152 R. Li et al. Table 3 Geopositioning accuracy: integration of IKONOS and QuickBird satellite images with a single aerial image and a pair of stereo aerial images ID 3-D Accuracy Addition of single AI (RMSE: meters) 3-D Accuracy Addition of stereo pair of AI (RMSE: meters) Combination and Convergence Angle σ x σ y σ z σ x σ y σ z 1 IKONOS (R) QuickBird (R) (27.5 ) 2 IKONOS (F) QuickBird (F) (11.8 ) 3 IKONOS (S) (30.2 ) 4 IKONOS (R) QuickBird (F) (37.7 ) 5 IKONOS (F) QuickBird (R) (56.8 ) 6 QuickBird (S) (61.6 ) Note: S stereo, F forward-looking, R rear-looking, AI aerial images of aerial images and their relative positions. In this configuration, the impact of the convergence angle formed by the two satellite images is decreased by the aerial images that significantly influenced the geometry. Applications in 3-D Shoreline Extraction and Evaluation To support coastal applications, the QuickBird and IKONOS stereo image pairs that cover the shoreline in the study area were employed to extract two 3-D shorelines (one from each pair) manually. To evaluate the vertical accuracy of the derived shoreline, bathymetry data collected by a water-penetrating LiDAR system (NASA s Experimental Advanced Airborne Research LiDAR system) and water gauge observations at Port Manatee, Port St. Petersburg, and Port of Tampa along the Tampa Bay area were used for comparison. The vertical datum for the shoreline is NAVD88. Figure 7 shows the coverage of the IKONOS and QuickBird images and the LiDAR data. The availability of the LiDAR bathymetry data and gauge data provides a unique opportunity to check the quality of the derived shoreline. Figure 8 shows one of the regions with derived shorelines from the QuickBird and IKONOS images overlaid on the LiDAR bathymetry. The legend of the LiDAR bathymetry is also shown in the figure. It can be seen that, horizontally, the shoreline is well overlaid around the same bathymetry interval ( 0.3

11 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 153 Figure 6. RMSE values obtained from the integration of the satellite images with (a) one aerial image and (b) a stereo pair of aerial images. m and 0.2 m). Considering the vertical geopositioning accuracy of QuickBird images of 0.75 m and that of IKONOS images of 1.06 m (see Table 2), this is well within the potential of the satellite images. This consistency was shown along the whole stretch of the derived shoreline. Figure 9 shows the comparison between the elevations of the derived shorelines and the water levels observed at the closest gauge stations at the time when the images were taken. Observations of three gauge stations were compared to the elevations of the IKONOS shoreline. At the closest gauge station, Port Manatee, only extrapolated water level data are available at the imaging time. Therefore, real observations from the two other nearest gauge

12 154 R. Li et al. Figure 7. Coverage of LiDAR bathymetry within IKONOS and QuickBird images. Figure 8. Comparison between derived shorelines and LiDAR bathymetry.

13 Figure 9. Comparisons between the elevations of water levels at the closest gauge station and the shorelines derived from (a) QuickBird and (b) IKONOS imagery. (Continued) 155

14 Figure 9. (Continued) 156

15 Table 4 Comparisons between shoreline elevations and water levels from gauge stations Data Shoreline Elevation Average Shoreline Elevation St. Dev. Water Level from Nearest Gauge Station Difference in Elevation QuickBird shoreline m m Port Manatee: m m m IKONOS shoreline m m Port Manatee: m (extrapolated) St. Petersburg: m m Port of Tampa: m m 157

16 158 R. Li et al. stations, Port St. Petersburg and Port of Tampa, also were used in this comparison to ensure the quality of the comparison. Table 4 lists some of the statistic values of the comparison. Compared to the corresponding water levels, the IKONOS shoreline is on average 0.5 m higher and the QuickBird shoreline is on average 0.2 m lower. Both resulting differences are within the vertical geopositioning accuracies of the IKONOS and QuickBird stereo images (see Table 2). Discussion and Conclusions In this study, stereo pairs of QuickBird and IKONOS satellite images along with 24 aerial images of the same region were used to examine the geopositioning accuracy for various geometric configurations of satellite and aerial images, including an experiment to test whether a single aerial image and/or a stereo pair of aerial images would help increase the geometric potential of the satellite images. Based on the results achieved, it can be observed that the geopositioning accuracy of the integrated IKONOS and QuickBird stereo pairs lies between those of the IKONOS and QuickBird stereo pairs, being better than that of the IKONOS pair, but not better than that of the QuickBird pair. Further, it was found that the geopositioning accuracy obtained by integrating four satellite images with all the aerial images is better than that of the satellite images (both single pairs and the integrated pairs) but not better than that of all aerial images alone. Therefore, it can be concluded that for the investigated circumstances, the accuracy of stereo images can be improved by the addition of images acquired by higher resolution sensors (either satellite or aerial). What is more interesting is that, instead of all the aerial images, only one single aerial image or a single stereo pair of aerial images is needed to integrate with the satellite images to achieve an improved accuracy. The imaging geometry and geopositioning potential are affected by many factors including resolution (centimeters to 1 m), orbit elevation (450 km for QuickBird and 680 km for IKONOS) or flying height (3.6 km), and convergence angle. The results also demonstrated that there is not a clear relationship between the convergence angle of a stereo pair and the geopositioning accuracy in this study because of the additional aerial images involved. The shorelines derived from the high-resolution satellite images were compared with water-penetrating LiDAR data and data from the water gauge stations. The results showed that the derived shorelines, particularly the vertical positions, matched with the coastal data from the two types of sensors, within the vertical uncertainty of the high-resolution satellite images. Acknowledgements The aerial images were provided by the Florida Department of Transportation. The water gauge station observations were provided by the National Oceanic and Atmospheric Administration. This research has been supported by the National Science Foundation and the National Geospatial-Intelligence Agency. References Di, K., R. Ma, and R. Li. 2003a. Rational functions and potential for rigorous sensor model recovery. Photogrammetric Engineering & Remote Sensing 69(1):33 41.

17 Integration of Aerial and Satellite Images and Application in Shoreline Mapping 159 Di, K., R. Ma, and R. Li. 2003b. Geometric processing of IKONOS Geo stereo imagery for coastal mapping Applications. Photogrammetric Engineering & Remote Sensing 69(8): Dial, G IKONOS satellite mapping accuracy. Proceedings of ASPRS Annual Convention 2000, May 22 26, Washington, DC, CD-ROM. Fraser, C Status of high-resolution satellite imaging. Photogrammetric Week 99, D. Fritsch and D. Hobbie (eds), pp Heidelberg: Wichmann Verlag. Fraser, C. S. and H. B. Hanley Bias compensation in rational functions for IKONOS satellite imagery. Photogrammetric Engineering & Remote Sensing 69(1): Li, R Potential of high-resolution satellite imagery for national mapping products. Photogrammetric Engineering & Remote Sensing 64(2): Li, R., F. Zhou, X. Niu, and K. Di Integration of IKONOS and QuickBird imagery for geopositioning accuracy analysis. Photogrammetric Engineering & Remote Sensing 73(9): Li, R., K. Di, and R. Ma D shoreline extraction from IKONOS satellite imagery. The 4th Special Issue on Marine & Coastal GIS, Journal of Marine Geodesy 26(1/2): Niu, X., F. Zhou, K. Di, and R. Li D Geopositioning accuracy analysis based on integration of QuickBird and IKONOS imagery. Proceedings of the ISPRS Workshop for High Resolution Earth Imaging for Geospatial Information, May 17 20, Hannover, Germany. Niu, X., K. Di, J. Wang, J. Lee, and R. Li Geometric modelling and photogrammetric processing of high-resolution satellite imagery. Proceedings of the XXth Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS 2004), July 12 23, Istanbul, Turkey, unpaginated CD-ROM. Noguchi, M., C. S. Fraser, T. Nakamura, T. Shimono, and S. Oki Accuracy assessment of QuickBird stereo imagery. The Photogrammetric Record 19(106): Robertson, B Rigorous geometric modeling and correction of QuickBird imagery. Proceedings of the International Geoscience and Remote Sensing IGARSS 2003, July 21 25, Toulouse, France, (Toulouse: CNES), unpaginated CD-ROM. Tao, C. V. and Y. Hu A comprehensive study of the rational function model for photogrammetric processing. Photogrammetric Engineering & Remote Sensing 67(12): Tao, C. V., Y. Hu, and W. Jiang Photogrammetric exploitation of IKONOS imagery for mapping applications. International Journal of Remote Sensing 25(14): Wang, J., K. Di, and R. Li Evaluation and improvement of geopositioning accuracy of IKONOS stereo imagery. ASCE Journal of Surveying Engineering 131(2): Wolf, P. R. and B. A. Dewitt Elements of photogrammetry with applications in GIS (3rd edition). McGraw-Hill Science Engineering, 608 p.