Full Text:   <1698>

CLC number: K826.14

On-line Access: 2008-03-26

Received: 2007-09-03

Revision Accepted: 2008-01-07

Crosschecked: 0000-00-00

Cited: 4

Clicked: 3634

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.6 P.858~866

http://doi.org/10.1631/jzus.A071469


An integrated classification method for thematic mapper imagery of plain and highland terrains


Author(s):  Shan-long LU, Xiao-hua SHEN, Le-jun ZOU, Chang-jiang LI, Yan-jun MAO, Gui-fang ZHANG, Wen-yuan WU, Ying LIU, Zhong ZHANG

Affiliation(s):  Department of Earth Sciences, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   zoulejun2006@zju.edu.cn

Key Words:  Image classification, Land cover and land use, Thematic mapper imagery, Plain and highland terrains, Integrated classification method


Share this article to: More <<< Previous Article|

Shan-long LU, Xiao-hua SHEN, Le-jun ZOU, Chang-jiang LI, Yan-jun MAO, Gui-fang ZHANG, Wen-yuan WU, Ying LIU, Zhong ZHANG. An integrated classification method for thematic mapper imagery of plain and highland terrains[J]. Journal of Zhejiang University Science A, 2008, 9(6): 858~866.

@article{title="An integrated classification method for thematic mapper imagery of plain and highland terrains",
author="Shan-long LU, Xiao-hua SHEN, Le-jun ZOU, Chang-jiang LI, Yan-jun MAO, Gui-fang ZHANG, Wen-yuan WU, Ying LIU, Zhong ZHANG",
journal="Journal of Zhejiang University Science A",
volume="9",
number="6",
pages="858~866",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A071469"
}

%0 Journal Article
%T An integrated classification method for thematic mapper imagery of plain and highland terrains
%A Shan-long LU
%A Xiao-hua SHEN
%A Le-jun ZOU
%A Chang-jiang LI
%A Yan-jun MAO
%A Gui-fang ZHANG
%A Wen-yuan WU
%A Ying LIU
%A Zhong ZHANG
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 6
%P 858~866
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A071469

TY - JOUR
T1 - An integrated classification method for thematic mapper imagery of plain and highland terrains
A1 - Shan-long LU
A1 - Xiao-hua SHEN
A1 - Le-jun ZOU
A1 - Chang-jiang LI
A1 - Yan-jun MAO
A1 - Gui-fang ZHANG
A1 - Wen-yuan WU
A1 - Ying LIU
A1 - Zhong ZHANG
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 6
SP - 858
EP - 866
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A071469


Abstract: 
The classification of thematic mapper imagery in areas with strong topographic variations has proven problematic in the past using a single classifier, due to the changing sun illumination geometry. This often results in the phenomena of identical object with dissimilar spectrum and different objects with similar spectrum. In this paper, an integrated classification method that combines a decision tree with slope data, tasseled cap transformation indices and maximum likelihood classifier is introduced, to find an optimal classification method for thematic mapper imagery of plain and highland terrains. A Landsat 7 ETM+ image acquired over Hangzhou Bay, in eastern China was used to test the method. The results indicate that the performance of the integrated classifier is acceptably good in comparison with that of the existing most widely used maximum likelihood classifier. The integrated classifier depends on hypsography (variation in topography) and the characteristics of ground truth objects (plant and soil). It can greatly reduce the influence of the homogeneous spectrum caused by topographic variation. This integrated classifier might potentially be one of the most accurate classifiers and valuable tool for land cover and land use mapping of plain and highland terrains.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E., 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper. A Revision of the Land Use Classification System as Presented in U.S. Geological Survey Circular 671. United States Government Printing Office, Washington, D.C., p.964.

[2] Bolstad, P.V., Lillesand, T.M., 1991. Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 57:67-74.

[3] Cohen, W.B., Spies, T.A., Fiorella, M., 1995. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. International Journal of Remote Sensing, 16(4):721-746.

[4] Crist, E.P., Cicone, R.C., 1984. Application of the tasseled cap concept to simulated thematic mapper data. Photogrammetric Engineering and Remote Sensing, 50(2):343-352.

[5] Dymond, C.C., Mladenoff, D.J., Radeloff, V.C., 2002. Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sensing of Environment, 80(3):460-472.

[6] Franklin, S.E., Peddle, D.R., Moulton, J.E., 1989. Spectral/ geomorphometric discrimination and mapping of terrain: a study in Gros Morne National Park. Canadian Journal of Remote Sensing, 15(1):28-42.

[7] Friedl, M.A., Brodley, C.E., 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61(3):399-409.

[8] Giannetti, F., Montanarella, L., Salandin, R., 2001. Integrated use of satellite images, DEMs, soil and substrate data in studying mountainous lands. International Journal of Applied Earth Observation and Geoinformation, 3(1):25-29.

[9] Huang, C.Q., Wylie, B., Yang, L.M., Homer, C., Zylstra, G., 2002. Derivation of a tasseled cap transformation based on Landsat 7 at satellite reference. International Journal of Remote Sensing, 23(8):1741-1748.

[10] Hutchinson, C.F., 1982. Techniques for combining Landsat and ancillary data for digital classification improvement. Photogrammetric Engineering and Remote Sensing, 48(1):123-130.

[11] Kauth, R.J., Thomas, G.S., 1976. The Tasseled Cap—A Graphic Description of the Spectral Temporal Development of Agricultural Crops as Seen by Landsat. Proc. Symp. on Machine Processing of Remotely Sensed Data, West Lafayette, Indiana. Laboratory for Applications of Remote Sensing, Purdue University, p.41-51.

[12] Lu, S.L., Shen, X.H., Zou, L.J., 2006. Land cover change in Ningbo and its surrounding area of Zhejiang Province, 1987~2000. Journal of Zhejiang University SCIENCE A, 7(4):633-640.

[13] McIver, D.K., Friedl, M.A., 2002. Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sensing of Environment, 81(2-3):253-261.

[14] Pal, M., Mather, P.M., 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4):554-565.

[15] Peng, W.L., 1991. Computer Processing of Remote Sensed Data and Geographic Information System. Beijing Normal University Press, Beijing, p.132-139 (in Chinese).

[16] Ramadan, E., Feng, X.Z., Cheng, Z., 2004. Satellite remote sensing for urban growth assessment in Shaoxing City, Zhejiang Province. Journal of Zhejiang University SCIENCE, 5(9):1095-1101.

[17] Rogan, J., Franklin, J., Roberts, D.A., 2002. A comparison of methods for monitoring multitemporal vegetaion change using Thematic Mapper imagery. Remote Sensing of Environment, 80(1):143-156.

[18] Seto, K.C., Kaufmann, R.K., 2005. Using logit models to classify land cover and land-cover change form Landsat Thematic Mapper. International Journal of Remote Sensing, 26(3):563-577.

[19] Shrestha, D.P., Zinck, J.A., 2001. Land use classification in mountainous areas: Integration of image processing, digital elevation data and field knowledge (application to Nepal). International Journal of Applied Earth Observation and Geoinformation, 3(1):78-85.

[20] Sohn, Y., Rebello, N.S., 2002. Supervised and unsupervised spectral angle classifiers. Photogrammetric Engineering and Remote Sensing, 68(12):1271-1280.

[21] Wang, Y.Q., Civco, D.L., 1994. Evidential reasoning-based classification of multi-source spatial data for improved land cover mapping. Canadian Journal of Remote Sensing, 20(4):381-393.

[22] Wilkinson, G.G., 2003. Are Remotely Sensed Image Classification Techniques Improving? Results of a Long Term Trend Analysis. Advances in Techniques for Analysis of Remotely Sensed Data, IEEE Workshop, p.30-34.

[23] Zhang, B., Valentine, I., Kemp, P., Lambert, G., 2006. Predictive modeling of hill-pasture productivity: integration of a decision tree and a geographical information system. Agricultural Systems, 87(1):1-17.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE