[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7848/ksgpc.2013.31.6-2.521

Rule set of object-oriented classification using Landsat imagery in Donganh, Hanoi, Vietnam

Thu, Trinh Thi Hoai (Hanoi University for Natural Resources and Environment)
Lan, Pham Thi (Hanoi University of Mining and Geology)
Ai, Tong Thi Huyen (International Centre for Advance Research on Global Change)

Publication Information

Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.31, no.6_2, 2013 , pp. 521-527 More about this Journal

Abstract

Rule set is an important step which impacts significantly on accuracy of object-oriented classification result. Therefore, this paper proposes a rule set to extract land cover from Landsat Thematic Mapper (TM) imagery acquired in Donganh, Hanoi, Vietnam. The rules were generated to distinguish five classes, namely river, pond, residential areas, vegetation and paddy. These classes were classified not only based on spectral characteristics of features, but also indices of water, soil, vegetation, and urban. The study selected five indices, including largest difference index max.diff; length/width; hue, saturation and intensity (HSI); normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) based on membership functions of objects. Overall accuracy of classification result is 0.84% as the rule set is used in classification process.

Keywords

Object-oriented classification; Rule set; Land cover; HSI;

Citations & Related Records

Reference

1	As-syakur, A. R., Adnyana, I. W. S., Arthana, I. W. and Nuarsa, I. W., (2012), Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area, Remote Sensing, No. 4, pp. 2957-2970.
2	Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I. and Heynen, M., (2004), Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, No. 58, pp. 239-258.
3	Dehvari, A. and Heck, R. J., (2009),Comparison of object based and pixel based infrared airborne image classification methods using DEM thematic layer, Journal of Geography and Regional Planning, No. 2, pp. 86-96.
4	Gaurav, K.P. and Prasun, K. G., (2010), Comparison of Advanced Pixel based (ANN and SVM) and Object oriented classification approaches using Landsat 7ETM data, International Journal of Engineering and Technology, No. 2, pp. 245-251.
5	Trimble Germany., (2011), eCognition Developer 7 Reference book.
6	Masek, J. G., Lindsay, F.E. and Goward S.N., (2000), Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations, International Journal of Remote Sensing, No. 21, pp. 955-969.
7	Chena, M., Sua, W., Lia,L., Zhanga, C.,Yuea, A. and Lia, H., (2009), Comparison of Pixel based and Object oriented knowledge based classification methods Using Spot 5 imagery, Wseas trasaction on information science and applications, No. 6, pp. 447-459.
8	Cu, P.V., Hang, N.T.T. and Dong, N.P., (2009), Comparison of Pixel Based and Object Oriented Classifications in Land Cover Mapping in the Red River Delta - Example of Duy Tien District, Ha Nam Province, Vietnam, 7th FIG Regional Conference. Hanoi.
9	Jackson, R. D. and Huete, A. R., (1991), Interpreting vegetation indices, Preventive Veterinary Medicine, No. 11, pp. 185-200.
10	Congalton, R. G. and Green, K., (2000), Assessing the accuracy of remotely sensed data - principles and practices, Taylor & Francis Group.
11	Sazzad, T. M. S., Islam, S., Mamun,M.M.R.K. and Hasan, Md. Z., (2013), Establishment of an Efficient Color Model from Existing Models for Better Gamma Encoding In Image Processing, International Journal of Image Processing, No. 7, pp. 90-100.
12	Xu, H., (2008), A new index for delineating built-up land features in satellite imagery, International Journal of Remote Sensing, No. 29, pp. 1381-1391.
13	Zha, Y., Gao, J. and Ni, S., (2003), Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, No. 24, pp. 583-594.