2. Test Data
2.1 Melbourne Dataset
The first dataset comprised a stereopair of epipolar-resampled Geo PAN images of Melbourne. As indicated in Table 1, the sensor and sun elevation angles for the stereopair (imaged in winter) were less than optimal. The azimuths of sensor and sun of the left image differed considerably, leading to strong shadows in non-occluded areas. The base/height ratio of the stereopair was 1.2. GCPS measured with an accuracy of 1-2 dm in both image and object space existed and could be used for our IKONOS sensor model and aerial image orientation. More details on the dataset and the GCPs can be found in Fraser et al. (2002). An existing stereopair of 1:15,000 scale wide-angle colour aerial photography was used to measure reference data.
2.2 Nisyros Dataset
This dataset consisted of two Pan-Sharpened Geo images of a Greek island with little man-made objects and sufficient vegetation. It is an non-active volcano with elevation range 0-700 m. The parameters of the images are listed in Table 2. As it can be seen, they were similar except of the sensor azimuth. The different viewing angle let to illumination differences between the two images. The fact that shadows were mostly in occluded areas was an advantage. The major multitemporal differences were due to different clouds and their shadows. 38 GCPs measured with GPS were available with an object accuracy of less than 1 dm. About half of them were not well defined in the image. The reference DTM had a grid spacing of 2 m and was interpolated from 1:5,000 map contours and additionally measured break lines. Its accuracy, estimated using the GCPs, was ca. 3.3 m RMS. More details on the data can be found in Vassilopoulou et al. (2002).
Table 1 Acquisition parameters of the Melbourne dataset
| |
Left Stereo |
Right Stereo |
| Date, Time (local) |
16/7/2000, 09:53 |
16/7/2000, 09:53 |
| Sensor azimuth (deg) |
136.7 |
71.9 |
| Sensor elevation (deg) |
61.4 |
60.7 |
| Sun azimuth (deg) |
38.2 |
38.3 |
| Sun elevation (deg) |
21.1 |
21.0 |
Table 2 Acquisition parameters of the Nisyros dataset
| |
Image 1 |
Image 2 |
| Date, Time (local) |
8/4/2000, 10:35 |
28/3/2000, 10:34 |
| Sensor azimuth (deg) |
134.9 |
72.1 |
| Sensor elevation (deg) |
73.5 |
72.7 |
| Sun azimuth (deg) |
136.6 |
138.7 |
| Sun elevation (deg) |
53.4 |
49.3 |
3. Own Matching Method and Test Results with Nisyros Dataset
3.1 Quasi-Epipolar Image Generation
Unlike frame-based imagery, where all pixels in the image are exposed simultaneously, each scan line of the IKONOS image is collected in a pushbroom fashion at different instant of time. Thus, epipolar lines with linear CCDs become curves. Using Orun and Natarajan' sensor model (Orun and Natarajan, 1994), which models the position of the sensor and its yaw angle variation as second-order polynomials of scan line (or time), and assuming the pitch and roll angles to be constant, the derived epipolar curve for a certain point shows a hyperbola-like shape. It can be approximated by a straight line for a small length but not for the entire image. An epipolar curve for the entire image can be approximated only by piecewise linear segments. In our approach, the epipolar curve for point (x
l, y
l) in the left IKONOS image is approximated by a quadratic polynomial and has the following form:
yl=
(A1x1+A2y1+A3)+
(A4x1+A5y1+A6)xl+
(A7x1+A8y1+A9)xl2 (1)
Where, (x
r, y
r) are the pixel coordinates in the right IKONOS image and A
1-9 are unknown parameters. Using Eq. (1), the quasi-epipolar image pair can be generated by re-arranging of the original IKONOS image pair. However, the computation of the unknown parameters of the epipolar geometry needs a certain number of well distributed conjugate points. These conjugate points are extracted by the following feature-point based matching:
-
The Moravec interest operator is used to select well defined feature points that are suitable for image matching. For this, the left IKONOS image is divided into small image windows of 21 ´ 21 pixels and then only one feature point, which has the highest interest value is extracted in each image window.
- Conjugate points are generated using the maximum of the normalized correlation coefficient. The positioning of the search areas is determined by using the already known control points in the neighborhood (image pyramids and a matching strategy based on region growing, which takes the already manually measured control points as seed points are used to get these approximate points). For reliability, the threshold of acceptable normalized correlation coefficients is 0.9.
- Least squares matching is finally used to refine the image coordinates of these points in order to achieve sub-pixel accuracy.
This procedure results in several hundreds of conjugate points. These points can be used to recover the epipolar geometry and interpolate the approximate values for the following grid point matching procedure.