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CLASSIFICATION OF LAND TYPES IN MINERAL AREAS BASED ON CART 
Wenbo Wu, Yuping Chen, Jiaojiao Meng, Tingjun Kang 
School of Geomatics, Liaoning Technical University, Fuxin, Liaoning, China, chneyuping317@163.com 
KEY WORDS: Remote sensing images, Knowledge, Knowledge based classification method, Classification and Regression Tree, 
Land types of the mineral areas 
ABSTRACT: 
The accurate classification of land types in mineral areas is very important to develop the mineral resources and monitor the 
environment of mineral area. Based on the spectral characters and the spatial knowledge of the ground objects in a certain mineral 
area in Shenyang which served as a training area. The spectral characters, texture characters, digital elevation model and slope are 
selected and extracted. The defined training sample areas are picked up by stratified random sampling techniques based on 
geographical coordinates. Firstly, using Classification and Regression Tree (CART) to discovery classification rules which 
integrates spectral, textural and the spatial distribution characters from these samples. Then, the interpretation was performed by a 
judgement based on these rules. Finally, the traditional supervised— Maximum Likelihood Classifier (MLC) was performed to 
check the classification accuracies. The results have suggested that the accuracy of classification based on the CART is higher and it 
can obtain a lot of reasonable rules most quickly and effectively. From the highly accurate classification results with multitemporal 
Landsat TM images we can detect the change of water bodies, vegetation, road, mining area, residential area and so on. So, it can 
provide useful data for the development and protection of the mineral resources. 
1. INTRODUCTION 
The excessive development of mineral resources is having 
initiated the grave resource environment problem. The precise 
classification of mining land is essential to develop the mineral 
resources and monitor the environment of mineral area. At 
present, a lot of scholars inside and outside have conducted the 
research regarding this , there are two methods they always 
used: One is to make use of aerial photograph or high 
resolution satellite image to establish interpretation markt by 
lettering, logical analyzing and field inspecting on the same 
scale topographic map, then extract the types of terrain feature 
by visual interpretation. The classification accuracy of this 
method is high, but it wastes time and energy [1]. The other is 
to make use of the image features after transforming to build 
classification template for each surface feature, and then use the 
maximum likelihood classification to classify. The main 
alternation methods include Principal Component 
Anlysis(PCA), Tasseled Cap, Ratio Method and so on [2] . The 
classification accuracy of this method is not high , and it isn’t 
applicable for distributed and scattered mining ares. 
With commercial high resolution satellite appearing, we will 
also be confronted with especially austere challenge by 
handling a great deal of remote sensing data while enjoying the 
advance of science and technology[3]. How to extract what we 
need information and discover implicit knowledge from a mass 
of data is the key question of remote sensing image 
interpretation at present. Such, data mining and knowledge 
discovery technology which emerged in 1980s have being 
introduced to the image processing of remote sensing, and the 
decision tree as a type of data mining technology was led into 
this field just 10 years ago.The decision tree method has many 
merits,such as building-up quickly, high accuracy, can creating 
understandable trules, the little amounts of calculation. It’s 
introduction can make full use of remote sensing data ,so it has 
an advantage in information extraction.The principle of 
decision tree is not to try to use one kind of algorithm or a 
decision rule to classify many category at a time, but is to carry 
out the most effective classification specifically for different 
aggregation choosing the different standard or method, so it can 
oversimplify with complicated problems and resovle 
completely [4]. 
There are many ways to structure decision tree. The 
comparatively mature algorithms include ID3 , C4.5 , C5.0 
series brought forward by Quinlan, Classification And 
Regression Tree (CART) , SLIQ and CHAID brought forward 
by Breiman and Friedman.The various algorithms have 
respective advantage and deficiency, and ID3, C4.5, C5.0 series 
are much uesed in remote sensing field, but these algorithms all 
adopt pruning-before, it needs to adjust the parameter carrying 
out trial again and again,so this research adopted CART 
pruning-after. 
2. IMAGE PREPROCESSING 
In the study,we chose TM image gained in 11th, August, 2001 
of this area as data source.Chose the 1:50000 scale 
topographical map of the area as the reference coordinate, and 
then carried out geometric precise correction. The amount of 
residual deviation was less than 5m, and the error didn’t exceed 
one pixel, this result satisfied the study. After the correction, the 
size of picture element was 30m, and then selected a trial area 
whose size was 256x256 pixel from it. Finally, digitized the 
contour lines on the 1:50» 000 scale topographical map, the 
contour inteval was 10m, then made use of these contour lines 
to creat DEM of the trial area., and then created slope view and 
aspect view based on this DEM. The overlay of TM’s false 
color composite imagery (RGB543) and DEM is as following 
figure 1.