In: Wagner W., Szdkely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B
.
244
< *-*
Dead reckoning Image referenced
georeferencing georeferencing
Dead reckoning
georeferencing
Reference
Figure 1. Georeferencing in GPS denied situation
Satellite
Resolution
M
Revisit time
M
Swath MdtlT
|km]
IKONOS (1999)
0.82
3~5
11.3
EROS-A (2000)
1.8
3-4
14
Quickbird (2001)
0.61
1-3.5
16.5
SPOT-5 (2002)
2.5
2-3
60
Orb View-3 (2003)
1
-3
8
FORMOSAT-2(2004)
2
1
24
CARTOSAT-1(2005)
2.5
5
30
ALOS PRISM (2006)
2.5
2-46
35
KOMPSAT-2 (2006)
1
4
15
EROS-B (2006)
0.70
3-4
7
WorldView-1 (2007)
0.50
4.6 (60cm)
17.6
CARTOSAT-2(2008)
0.80
4-5
9.6
GeoEye-1 (2008)
0.41
2.8 (50cm)
15.2
WorldView-2 (2009)
0.46
3.7 (52cm)
17.6
Table 1. Current high-resolution satellite imaging systems
In a recent research for automatic georeferencing of airborne
pushbroom scanner by Cariou and Chehdi (2008), the reference
data is transformed into the acquired image domain using the
initial EOPs from INS, and then mutual information is
computed between the transformed reference and acquired
image. Through iteration of this computation and image
transformation, a pixel-to-pixel correspondence is obtained and
used for estimating yaw angle, and small bias in roll, pitch and
height constant. This approach requires good initial EOPs from
the INS and is computation intensive as a large number of
iterative image transformations are needed.
This study proposes the combination of SURF (Speeded-Up
Robust Features) (Bay et al., 2008) and RANSAC (Fischler and
Bolles, 1981) for robust image matching, and the collinearity
equation camera model with the Gauss-Markov stochastic error
model for the trajectory modeling of the airborne pushbroom
camera. The paper is structured as follows. First, the proposed
method is presented, including a brief description on the image
matching and platform trajectory modeling. Second,
experimental results on simulation data are discussed, followed
by a brief conclusion.
2. HIGH RESOLUTION SATELLITE IMAGE AS
GROUND CONTROL INFORMATION
Since IKONOS-2 showed its potential in the commercial
satellite image market, many high-resolution satellite imaging
systems have been launched, see Table 1. The specification of
high-resolution satellite images is listed in terms of its spatial,
temporal resolution, and swath width. Note that many satellites
provide sub-meter resolution with large swath width of more
than 10 km. In addition, positioning accuracy has seen a steady
increase over the years. For example, GeoEye-1 provides RPC
with positional accuracy up to 2 m of circular error at a 90%
confidence level (CE90) without GCP in the case of stereo
images, and sub-meter accuracy could be achieved using a bias-
compensation RFM model with a single GCP (Fraser and
Ravanbakhsh, 2009). Moreover, higher performance satellites
will be launched in the near future such as CARTOSAT-3,
EROS-C, and GeoEye-2. These attractive capabilities motivate
the idea of using high resolution satellite images as ground
control information for other geospatial images, such as aerial
images. In the navigation field, research has started on testing
and suggesting the use of satellite imagery to support UAV
navigation (Sim et al., 2002; Conte and Doherty, 2008).
3. PROPOSED METHOD
Figure 2 shows the flowchart of the proposed method. As direct
georeferencing is unavailable, due to GPS denied condition, and
the reference data becomes available, short duration of HSI and
INS is processed for georeferencing purposes. Though the INS
data is drifting without georeferencing fixes, it can still provide
good approximation for georeferencing, and thus reference data
is windowed with an error margin, so the image matching with
the raw HSI is limited to a smaller reference area (subset).
During image matching, SURF is utilized with RANSAC to
mitigate the effect of mismatched points. Successful image
matching provides ground control information for each extracted
raw image point, and thus the trajectory and attitude are estimated
based on this information.
Figure 2. Flowchart of the proposed approach
3.1 Subset ROI reference data
Since images used as reference data tend to be large, it would
take too much time and require a lot computer power if the
whole reference image is used for image matching, fortunately,
the INS-based estimation of location can provide good
approximation to obtain a region of interest (ROI), using the
inverse form of collinearity equation. The ground coordinate of
ROI can be determined from Equation 1. The ground height
information could be selected as a constant value from
knowledge about the target area; note that error of the height
will be compensated in the error margin terms.