191
Water body
Paddy field
Arid land
Building area
Road
Vegetation
Subsi deneeland
Figure 3(b). The output image classified by Supervised
Classification
4.4 Accuracy Assessment
Accuracy assessment is one of the indispensable jobs in the
process of the remote sensing data classification.Through
accuracy assessment, the classifying person can ascertain the
validity of the classification signature, improve the
classification signature, enhance classification accuracy; the
user can gain information in the classified image correctly and
effectively according to the classification accuracy. [l21 The
method based on the confusion matrix is universally
recommended classification accuracy assessment method. In
this study, we chose 1,024 detecting samples randomly
referring to TM image and 1:50000 topographic maps, then
through visual interpretation to structure the confusion matrix,
carried out classification accuracy assessment based on the
correlation index calculated.
5. CONCLUSIONS
This study used the thought of CART analysis whose tree
shaped was simple,clear and intuitionistic. It caused the multi
characteristic and multi-model land types of the trial area to be
clearer, so it easily realized the automatic recognition in the
compute.
This study used CART analysis to classify and extract the
mining area land resources, had yielded some result, specially
had extracted the subsidence lands.But,because of the time and
insufficiently experienced myself, the CART decision tree was
not too perfect. After ground truth investigation, we found one
subsidence land was omited and two arid lands had classified
into subsidence land by mistake, but this thought was feasible,
in later work can improve the CART decision tree shape, cause
it to be more perfect.
REFERENCES :
[1] Dazhi Guo, Yehua Sheng, Mingxing Hu etc., 1998. Mining
Area Environment Disaster Dynamic Monitor and Analysis
Assessment. The press of Chinese Mining University, Xuzhou.
[2] Yinhui Zhang, Gengxing Zhao, 2002a. The summary of
remote sensing data classification methods about land use/ land
cover. Chinese agricultural resources and districts, 23(3), pp.
21-25.
[3] Kaichang Di, 2001. Spatial Data Mining and Knowledge
Discovery. The press of Wuhan University, Wuhan.
[4] Yurong Gao, 2006b. Study on land use information
extraction based on decision tree method. Dissertation
Submitted to Zhejiang University For Degree of Master,
Zhejiang.
[5] Breiman L, Friedman J H, Olshen R A, etc., 1984.
Classification and Regression Tees. Monterey, California,
U.S.A.: Wadsworth International Group, pp. 1-358.
[6] Yohannes Y, Hoddinott J, 1999. Classification and
Regression Tree: An Introduction. Washington, D.C., U.S.A.:
International Food Policy Research Institute.
[7] Ping Zhao, 2003b. Knowledge-based Landuse/cover
Classification in the Typical Testareas of the Lower Reaches of
Yangtze River. Dissertation Submitted to Nanjing University
For Degree of Doctor, Nanjing.
[8] Haralic R M, Shanmugam K, 1973a. Dinstein I Texture
Features for Image Classification. IEEE Tmnsactions on
Systems, Man and Cybernetics, (6), pp. 610-621.
[9] Deshen Xia, Desheng Fu, 1997. The Modern Image
ProcessingTtechnology and Application. The press of
Southeast University, Nanjing.
[10] Treitz P, Howarth P, 2000a. Integrating Spectral Spatial
and Terrain Variables for Forest Ecosystem Classification.
Photogrammetric Engineering & Remote Sensing, 66(3), pp.
305-317.
[11] Franklin S E, Pebble D R, 1989a. Spectral Texture for
Improved Class Discrimination in Complex Terrain.
International Journal of Remote Sensing, 54, pp. 1727- 1734.
[12] Jianping Wu, Xingwei Yang, 1995a. Accuracy analysis of
classification of remote sensing data. Remote Sensing
Technology and Application, 10(1), pp. 17-24.