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Proceedings Paper

Land use investigation with remote sensing based on spectral character analyses in Poyang Lake region, China
Author(s): Shu-e Huang; Baosheng Wang; Huaiqing Wang
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Paper Abstract

Based on Landsat TM data combined with practical investigation information obtained using Global Positioning Systems (GPS), we created a training field of land use classification. Using the methods of spectral distance analysis, we analyzed spectral signature value of different training fields in TM3, TM4, TM5 and TM7 band, and compared these with the standard deviation analysis. Based on these results, we selected the best spectral bands for classification and created remote sensing interpretation marks of land use classification. Supervising classification was used with the image classification of TM and the maximum likelihood was used for parametric rule of supervised classification. We applied the method of spectral signature analysis to the individual study of land use classification of Poyang Lake region. The land use was classified into 9 classes: paddy field, non-irrigated farmland, forestland, grassland, water area, lake beach, grass beach, sandy land and residential area. Based on the data of GPS investigation, we assessed the classification accuracy. Result indicated that classification accuracy reached 91.43% and the classification effect was better than the common supervised classifying and unsupervised classifying.

Paper Details

Date Published: 22 December 2003
PDF: 8 pages
Proc. SPIE 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, (22 December 2003); doi: 10.1117/12.506434
Show Author Affiliations
Shu-e Huang, Meteorological Research Institute of Jiangxi Province (China)
Baosheng Wang, Meteorological Observatory of Jiangxi Province (China)
Huaiqing Wang, Meteorological Research Institute of Jiangxi Province (China)


Published in SPIE Proceedings Vol. 5153:
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture
Wei Gao; David R. Shaw, Editor(s)

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