189 
Figure 1. The overlay of TM’s false color composite imagery 
(RGB543) and DEM 
3. THE PRINCIPLE OF CART 
In fact, CART is one kind of data surveying and forecasting 
algorithm (Breiman L, 1984), it not only can deal with the 
highly tilt and many states numerical value,but also can deal 
with homothetic attribute data in order or out of order. The 
CART algorithm adopts the technology of the dimidiate 
recurrent division, it always divides the current sample into two 
son-samples, this makes each non-leaf nodes that has two 
branches.The benefit of this algorithm is that it can take a 
portion as the training data, and the other one as the checkout 
data.lt leads into an "adjustable mistake rate " in process, it 
means that all leaf nodes of one branche joined a punishment 
factor.If that branch is still able to keep low mistake rate, then 
keep it,otherwise, give it up.The ultimate analysis result is an 
optimum binary decision tree which takes complicated degree 
and mistake rate into account, all approachs that equinoctial 
points define are corresponding to a most conditional class. 
So,the decision tree that CART creates is a concise binary 
decision tree. 
The particular description of CART is as following: 
/* T represents the current sample collection, 
T _ attribute list represents the current candidated attribute 
collection */ 
Function cartformtree (T) 
{ 
establishe root node N ; 
assign classes for N ; 
If T all belong to the same class OR only on sample left in 
T 
Then return N as leaf node and assign a class for it; 
For attribute in each T _ attributelist 
carry out a division for that attribute, calculate Gini 
index of that division ; 
the testing attribute of N equals to the attribute which has 
minimal Gini index among T _ attributelist; 
divide T into two son-collections , 7^ : 
transfer cartformtree (7]) 
transfer cartformtree (T 2 ) 
} 
CART has the following merits: limpid structure, easily 
understand,simply realize,quick speed, high accuracy; Can deal 
with a large amount of data and the nonlinearity relation.The 
data put in can be continuation variable also can be a discrete 
value; Contains the default and error of a data; Can give out the 
significance of the testing variable [5 , 6]. In the process of 
CART decision tree growth, it adopts Ginilndex which is 
always used in economics field to be the criterion testing 
variable and segmentation rule.The mathematics definition of 
Ginilndex is as following: 
J 
Ginilndex = x ~Hp 2 U\ h ) 
Where, p(^j\h^j is the probability when some one testing 
variable h belongs to the j class, Kj{h) is the sample 
number when some one testing variable h belongs to the j 
class, «(/?) is the sample number when some one testing 
variable belongs to j , J is the number of class in training 
sample collection. 
4. CLASSIFYING BASED ON CART 
4.1 The selection of training sample 
The selection of training sample was the essential step in the 
study, which directly related the rule quality gained. There still 
weren’t standard classification system for mining areas based 
on the remote sensing image, this article referred to the land use 
and land cover classification system. 
In order to study the overall situation of the mining land 
resource, considering the actual situation, the trial area land 
types was classified into seven classes according to the image 
interpretation ability.The land types included Water body, 
Paddy field, Arid land, Building area, Road,Vegetation and 
Subsidence land. 
In order to enable the training sample to reflect each kind of 
land type in the spatial distributed characteristic, this article 
used random delamination sampling method according to the 
space coordinates.Carried the sampling on the trial area 
referring to TM and 1:50,000 scale topographic map. 
4.2 Determining the testing variable 
The spectral response characteristic most direct affects the 
ground fetures identification ability of multispectral remote 
sensing image, and it is also the most important interpretation 
element.Each ground feature has the unique spectrum reflection 
and the radiation characteristic as a result of the different 
material composition and the structure, this reflects on the 
image is that each ground feature in various wave bands has 
different grey level. But because of the complexity of the 
ingredient and structure of ground features, as well as the 
influence of the remote sensing sensor and the atmospheric 
environment, the optical spectrum feature of the ground features 
present the multiple complex changes.Therefore, in order to 
make full use of the TM data to carry on the information