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This paper is organized as follows: Section 2 briefly
summarizes the automatic river feature extraction based on old
vector river map by profile matching. The mathematical model
and its solution of generalized point photogrammetry are
presented in Sections 3. In section 4, based presented method
above, some tests are carried out and the precision proved the
method is manipulable. Conclusion is drawn in section 5.
2. AUTOMATIC FEATURE EXTRACT
Extraction of curvilinear features such as rivers and roads has
been one of popular research topics in computer vision,
photogrammetry, remote sensing and GIS communities.
Automation of such extraction has been key research issue,
although full and reliable automation is yet to be achieved.
Various methods proposed for this theme include perceptual
grouping, (Trinder and Wang, 1998; Katartzis et al, 2001),
scale-space approaches (Mayer and Steger, 1998), neural
network and classification (Doucette et al., 2001), “snakes” or
energy minimization (Gruen and Li, 1997), and template
matching (Vosselman and Knecht, 1995; Gruen at al., 1995; Hu
et al., 2000). Those methods were always proposed for road
extraction. from aerial imagery or remote sensing imagery,
which were not always fit for river extraction from remote
sensing imagery.
In this paper, a new algorithm for river extraction was proposed
with two steps, which provide the observations for the exterior
orientation in the next section. First a global affine
transformation between remote sensing image and vector map is
determined by using three coarse conjugate point pairs defined
manually, which provide initial corresponding relation between
image and map for automatic linear feature extraction. In the
second steps, profiles matching is preferred for precise
extraction of river feature points based on the radiological
characteristic of river on the remote sensing imagery and the
initial position proposed in the first step.
Figure 1: river feature points extraction by profile matching,
the red lines are initial value provided by vector
road map; the green points are feature points
extraction by profile matching
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Im
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0013566653100
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(a) (b)
Figure 2: Stand Ridgelike Template (a) and the Generated
Model Profile over Maximising Cross Correlation (b)
On the remote sensing imagery, the characteristic of rivers are
not distinct like the roads, which are geometrically elongated,
have a maximum curvature, and radiologically have a
homogeneous surface and a good contrast to adjacent areas. In
this paper, the whole rivers net were not extracted whereas the
feature points of the rivers were extracted corresponding to the
projected river line segments beside the initial position
transformed through three coarse conjugate point pairs (Figure
1). The profile matching compares the model profile with the
river profile at the position on the normal orientation of the
initial river line segments. The model profile was generated
with a stand ridgelike template over maximising cross
correlation (Figure 2). The differences between the two profiles
are modelled by two geometric (shift and width) and two
radiometric (brightness and contrast) parameters. These
parameters are estimated by minimising the square sum of the
grey value differences between the profiles (Ackermann (1984)).
Least squares matching are preferred over maximising cross
correlation because least squares matching can estimate the
precision of the profile shift. This precision can be used to
evaluate the success of the matching algorithm and moreover is
required as weight for the exterior orientation. Another
advantage of least squares matching is the possibility to model
the geometric (and radiometric) transformation between the two
profiles. Not only the river position, but also the river width can
be estimated. Thus, when the road width is changing, least
squares matching can obtain good results while cross
correlation will fail (Figure 1).
3. MATHEMATICAL MODEL
Traditional photogrammetric exterior orientation procedures are
point based. The exterior orientation of a single image can be
determined by means of several control points. Their image
coordinates and ground coordinates are the observations in the
collinear equations, and the exterior orientation parameters are
computed in a least squares adjustment. In this research, the
exterior orientation is based on automatic linear objects
extraction and generalized point photogrammetry, so called
generalized point it means that collinear equation is still used
for linear primitives.
Some literature proposed line photogrammetry, which used
linear primitives to calculate the exterior orientation parameters
based on the principle that the object space point P lies on the
plane defined by the perspective center S and the two image
space points defining the image line (a and b) (Figure 3). The
