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Poster Session 2
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Comparative study on image data model for integrating multiple resolution image/raster data
2.2 Proposal of image data model
1) Fundamental configuration of image data model
The image data model consists of 2-dimensional pixel model and additional footprint boundary models.
2) 2-dimensional pixel model
2-dimensional model have two components; one is 2-doimensional array of pixel values and mapping function which relate the image coordinate values with the ground coordinate values. The mapping function is denoted by f(i,j) , {(i,j) = (line# and pixel#)} for transferring the image coordinate values to the corresponding ground values. The model also has an inverse function f1(x,y,z) ,
{(x,y,z): ground coordinate values}. These functions are usually non linear function due to the topographic distortions and so on. The boundary of footprint is indirectly or implicitly represented as the boundary of square a rectangle pixel on the image plane. Although operation algorithms consistent with model are not sufficient by provided, this model is conventionally used.
3) Footprint boundary models
The footprint boundary models represent the shape or the boundary of pixel on the ground surfaces clearly. This model is not used independently, but is always used as additional models to the basic 2-dimensional pixel model. We propose two types of model as follows, under this category.
- Grid line model
This model represents the line boundary and the pixel boundary of footprint the parallel straight lines with regular intervals on the 2-dimensional plane. This model has a directional vector, distance of each boundary, and the origin of the initial line and pixel boundary as parameter.
- Line segment model
This model approximates the shape of the footprint (the boundary of line and pixel) by line segments. To represent this model, we should make a table of coordinate values of segment end points (ground coordinate values). Operations based on this model can be conducted efficiently by generating an interim file as follows.
Ymn£y*<ym(n+1)
We assume each line segment is denoted by a series of end points with coordinate values as
(Xpij,Ypij),
(XLij,YLij),
(XL**,YL**) denotes the boundary of pixels, while
(XL**, YL**) is boundary of line, where I is boundary # (pixel#, line#), and j is the serial # of ends points of line segments. In Fig.4 the image data has pixel boundaries
(P0,P1,P2,…..) and line boundaries
(L0,L1,L2,…). The location of
P0 is represented by
(Xpoj, Ypoj), {j=0,1,2…..M}.
Fig.4 Line segment model
We will show how the intersection of the line (y=y*) between the boundary of pixel
(Pm) can be computed. First, find n satisfying the following inequalities. And, the solution of the following equations
(Xm(y=y*), y*) is the point of intersection.
| Xm(y=y*)= |
y*-ymn
-------------------
Ym(n+1)-ymn |
.(xm(n+1)-xm)+ xmn |
We will compute all x coordinate values of intersection points between the pixel boundaries and y=y* where
(y*=a0,
a1,
a2,…: sorted y coordinates of end of all pixel boundaries descending order) , with the above method. The same procedure is applied to compute all x coordinate values of intersection points with all line boundaries. With this process , we can generate the interim file shown as Table .1. When m is the number of pixel boundaries, n is line boundaries, and the total number of end points is M and N respectively for all the pixel boundaries and all the line boundaries, the maximum computational load is O (M=n+m=N).
3.The operation methods of the image data model
At this section, we list the operations necessary for the integrated use of multi-resolution image data with vector data, examine the computational load of the operations and the possible data quality degradation by resampling and overlaying.
Table 1. Intrim file for Line Segment Model
Line# Y |
0 |
1 |
2 |
... |
| b 0 |
X 0 0 |
X 0 1 |
X 0 2 |
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| b 1 |
X 1 0 |
X 1 1 |
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| b 2 |
X 2 0 |
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| : |
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pixel# Y |
0 |
1 |
2 |
... |
| a 0 |
X 0 0 |
X 0 1 |
X 0 2 |
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| a 1 |
X 1 0 |
X 1 1 |
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| a 2 |
X 2 0 |
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| : |
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