4. CONCLUSIONS
This study aims to carry out the image classification by using
the Formosat-2 satellite image and to suggest the effective
classification method for the vinyl greenhouse detection
through the accuracy comparison by the image classification
method.
The parallel-piped classification, minimum distance
classification, maximum likelihood classification, Maharanobis
distance classification among the supervised classification
methods and rule-based classification were carried out, and the
proper classification method for the vinyl greenhouse detection
was assumed. In addition, the misclassification item of two
classification methods was treated with complementary by
creating the image after connecting the results of the supervised
classification method and the rule-based classification. It can be
seen when Maharanobis distance classification and rule-based
classification were connected through the area comparison of
the results of each classification method and visual
interpretation detection, the vinyl greenhouse detection
accuracy can be improved. It is expected that it can be used
effectively for the vinyl greenhouse detection if the missing
parts in the connection process of the supervised classification
method and rule-based classification method can be
complemented in the future.
REFERENCES
Biao Oin, Yuni Xia, Sunil Prabhakar, Yicheng Tu, 2009, A
Rule-Based Classification Algorithm for Uncertain Data,
Proceedings of the 2009 IEEE International Conference on
Data Engineering, pp. 1633-1640.
Chen, H. W., Chang, N. B., Yu, R. F., Huang, Y. W., 2009,
Urban land use and land cover classification using the neural-
fuzzy inference approach with Formosat-2 data, Journal of
applied remote sensing, Vol. 3, No. 1, 033558.
Cho, E. R., Kim, K. W., Yoo, H. H., 2009, Analysis of Land
Use Change Using High Resolution Satellite Imagery, Journal
of the Korean Society for Geospatial Information System, 17(1),
pp. 3-11.
Hwang, H. H., 2007, The Accuracy Estimation of Landcover
Map for Reliability, Thesis of Master, Kyonggi University, pp.
1-66.
Lee, J. Y., Kim, B. S,, 2008, Automated Green House
Extraction Method Using Texture Information in High Spatial
Resolution Satellite Image, Proceeding of annual spring
conference of the Korean Society of Remote Sensing, pp. 48-52.
Li, Z., Hayward, R, Zhang, 1. lin, H.. Walker, R., 2010,
Evaluation of Spectral and Texture Features for Object-Based
Vegetation Species Classification Using Support Vector
Machines, /SPRS TC VII Symposium Vienna — 100 Years ISPRS,
IAPRS, Vol. XXXVIII, Part 7A, pp.122-127.
Oh, C. Y., Park, S. Y, Kim, H. S, Lee, Y. W., Choi, C. U.,
2010, Comparision of Landcover Map Accuracy Using High
Resolution Satellite Imagery, Journal of the Korean
Association of Geographic Information Studies, 13(1), pp. 89-
100.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
^ S
KE
AB
Bui
are
WO!
ach
Ve;
CIE
acc
dor
phe
(SH
are
the
