have ODBC, DAO, OLE C++ Oracle, OLEDB, ADO, 
PRO*C and OCI, etc. In view of the compatibility and 
efficiency in system development, this paper adopts OCI to 
develop Oracle data. 
3. THE KEY TECHNOLOGY RESEARCH ABOUT 
ESTABLISHING THE IMAGE DATABASE 
3.1 Block, coding and index of image data 
1) Block of image data 
In the huge image database, scheduling and the use of the 
image data is only a small part of the database. If the data files 
are very large, it will directly affect the speed of data read and 
implementation. Data block is the key image database 
technology to organize and manage the data efficiently. In view 
of practical problems of theory and application, image database 
is established to adopt data block size 128x128 pixels or 
256x256 pixels. 
Image data are to the production of maps for the unit, each is a 
map of orthophotos of the final products are stored as 
documents. Virtually every map can be seen as a Segment, but 
each piece of the map data is huge, maps internal data also are 
partitioned once again. The partition of image block has two 
ways: Strip division and massive division. Because massive 
division support graphics indexing and mosaics, and the 
division of Block has good aggregation properties, massive 
division is used to partition the data block in the image 
database. 
2) Coding of image data 
The basic principium of space coding is that image block is 
organized by some strategies. It is a process that 
two-dimensional space object mapped to a one-dimensional 
space in the light of a certain coding function. The most 
commonly methods of coding space data: Row Ordering, 
Morton Ordering and Hibert-Peano Ordering etc. Row 
Ordering and Morton Ordering are relatively simple. They 
support image and block access to data directly and regional 
inquiries. In order to manage and use the space data expediently, 
4D products are a standard topographic map to produce and 
data files are to Map No. name as the file name, in mapping 
fields. For every Orthophotomap ,we can code it with Row 
Ordering after partition of image according to a certain size , 
number the corresponding maps before each coding, then can 
constitute the second level coding of every block. 
It is impossible to partition into integral blocks with 128x128 
pixels or 256x256 pixels block because of the second level 
coding of maps and blocks. As shown in Figure 2. 
6 
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Fig 2.Code way of Row Ordering 
This paper the size of 1:2000 image is 1668 x 1668 pixels, it 
can be divided into a total of 7x7 = 49 image blocks with 256 x 
256 block to gather 2n * 2n forms(23 * 23 = 8x 8 = 64 forms), 
to meet Morton Ordering or Hilbert Ordering Coding. It need to 
increase the complexity of the algorithm to affect the index of 
the image blocks. So this paper use Row Ordering to code. This 
method is simple and support image block to access to data 
directly and regional inquiries. The rows and columns code 
from the lower left-hand comer of the map, from left to right 
increase gradually,col= 1,2,...from the bottom up increase 
gradually, row=0,1... 
3) The index of image data 
Window index is the most commonly data index way from the 
image database. Its core is to find image data coding in the 
window quickly. Then extract the corresponding image data 
block from the image database according to image blocks 
coding, setting the image blocks together, you can get the 
image window. The process of indexing the image blocks 
within the scope of window from the image database is shown 
in figure 3: 
Image 
Scale 
index 
Image 
Project 
Index 
Map 
index 
Image 
Types 
index 
Image 
block 
index 
Image 
block 
Record 
index 
Fig 3. Image Database Multi-Index 
3.2 Structure and Construction of image pyramid 
PCJL0CKID Jt 
PCJL00K 
4009-49502 
D7D389D7D38... 
4009-49503 
B28863CCM8... 
4009-49511 
B2BACCB2C8... 
Fig 1. Corresponding relation between block’s number and data 
block 
Image is classification stored and managed in the light of 
resolution ratio. The resolution ratio of the bottom is highest, 
and has the largest amount of data. The lower the smaller the 
volume of data, so different resolution radios remote sensing 
images form a tower structure. The structure is called the 
image pyramid. Remote sensing database is built with image 
pyramid structures which is easy to organize, store and 
manage the multi-dimensional, multiple data sources of remote 
sensing data, to achieve a cross-resolution of index and browse.