region, using knowledge base. These categories are the 
sub-classes of topographical map factors to be extracted. 
For example, settlement places are divided into asphalt 
and concrete roof settlement places. The third step is to 
merge the sub-classes of same map factors into one. The 
reseach scheme is shown in Fig. 1. 
The following sections are devoted to the production 
of texture energy image, the establishment of knowledge 
base and the decision making of classification. Principal 
component transformation and unsupervised classification 
algorithms are presented in relevant documents in detail. 
They will not be discussed here. 
PRODUCTION OF TEXTURAL IMAGE 
The Production of texture image is to enhance the 
image data of a band selected from multispectral images 
using rule-based localized enhance technique(Zhang et al, 
1991), and based on this to generate textural image 
using a texture energy technique. 
The texture energy technique was presented by Laws 
(Wechsler,1980) . The idea of this technique is similar to 
that of Fourier transformation, but it is implemented 
in space domain. Two steps are required for the 
implementation of this technique. 
The first step is to compute the convolution of the 
given image f(i,j) with mini-mask h(s, p) (e.g., Lapla- 
cian and gradiant operator, generally 3 x 3 or 5x 5) to 
detect the pixels on edges: 
N-1 N-1 
gi, = E, E f(i-s,j-p) hG. p) (1) 
where N is the size of the mini-mask. 
The second step is to abstract texture energy in a 
larger size window, which is defined as the square sum 
of grey levels within the window. The sum of absolute 
value is used to substitute for the square sum for the 
convenience of computation, then 
GG, - EE |8G-k,i-D) Q) 
where M is the size of the window. Generally M —9, 15 
or 21. 
In essence, the texture energy algorithm is to find out 
the pixels on edges and to regard the statistical signal 
energy as the apparent feature of textural signal. 
ESTABLISHMENT OF KNOWLEDGE BASE 
In order to use knowledge for automated extraction 
of map factors, a knowledge base must be established. 
Two things must be done: one is to research the spectral 
and textural properties of objects, the other is to repre- 
sent the inherent and interrelative features of objects. 
Each category of objects has its inherent spectral and 
textural properties. The properties can be acquired with 
visual interpretation experiences and the spectral 
reflectance curves of objects that have been known. In 
the following paragraph, the spectral properties of 4 
88 
ground objects(ie.,vegetation, soil, water and buildings) 
are discussed simply. 
Green vegetation. has a low reflectance in the 
0.35-to-0.7 um region with a slight rise at 0.554 m, and 
a higher reflectance in the near-infrared(0.75 — 1.354 m), 
but low reflectances at 14, 19 and 2.7um. The 
reflectance curve of soil is less complex than that of veg- 
etation. The reflectance of soil generally increases with 
the increasing of wavelength, especially in the 
0.35-to-1.4#m region. Because soil is a complex mix- 
ture, and has various types, the spectral reflectance fea- 
tures of which are different. Water has a low reflectance 
in the visible band and a tendency of decreasing in the 
near-infrared. Asphalt and concrete roofs of buildings 
have the reflectance profile typical of man-made materi- 
als, ie., a generally increasing reflectance with increasing 
wavelength. The reflectance varies with the age of the 
building. Roofs of natural materials have the same 
reflectance as the natural materials. All above can be 
found in relevant documents. 
Besides spectral properties for object interpretation in 
images, the textural properties are very important. For 
example, soil and buildings have the similar spectral 
properties, i.e.,a generally increasing reflectance with 
increasing wavelength, but their textural properties are 
quite different. 
In the knowledge base we have established, there are two 
kinds of knowledge, i.¢., band-to-band (BB) and category- 
to-texture (CT) ,which are expressed in terms of rules and 
weights. A rule is a comparision between band and band or 
texture and threshold. A weight is a supporting or 
opposing evidence weight when the rule is right or false. 
A knowledge sub-base for a certain category is com- 
posed of a set of rules and weights. For example, the 
knowledge sub-base for the forest interpretation in TM 
image is composed of 
rule 101: band(4)>sum (1,2,3) [50, 0] 
rule 103: band(2)> band (1) [10, 10] 
rule 106: min(4,5,6)<sum (1,2,3) [0, 50] 
rule 114: band(0) (texture image) « 20 [20, 10] 
where band(i) stands for the value of band i, sum( ) 
denotes the sum of the bands in parentheses, min( ) de- 
notes the minimum of the bands in parentheses, the first 
and second number in brackets| ] indicate the sup- 
porting and opposing evidence weight, respectively. The 
minimum weight is 0, and maximum weight is 100. A 
rule is a constraint in essence. According to the roles 
they play in category interpretation, constraints are di- 
vided into mandatory constraints, optional constraints 
and contradictory constraints. Mandatory constraints are 
represented by having the supporting and opposing 
weights greater than zero so that evidence is added for 
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