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REMOTE SENSING FOR LAND & RESOURCES    2000, Vol. 12 Issue (1) : 39-43     DOI: 10.6046/gtzyyg.2000.01.08
Technology and Methodology |
THE APPLICATION OF FUZZY CLASSIFICATION TO CROP CLASSIFICATION
You Shucheng, Zhang Wei, Yan Tailai
The Remote Sensing Center of China Agriculture University, 100094
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Abstract  

Fuzzy classification method is introduced in this paper. Contrasting with maximum likelihood statistic classification, fuzzy classification shows a better discrimination of crop by using muti-temporal ScanSARdata. Emphasis has been placed on the potential of the combination of contextual classification and fuzzy classification because of the individual feature of radar.

Keywords Hyperspectral      Spectral reconstruction      Atmospheric correction      Radiative transfer model      MDOTRAN 4.0      6S Model     
Issue Date: 02 August 2011
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ZHANG Chuan
LIU Shao-Feng
LIU Yan-Hong
PEI Xiao-Yin
YANG Fan
KONG Mu
LIU Hua-zhong
CEN Kuang
XU Ren-ting
SONG Yun-tao
WANG Cheng-wen
HAO Zhi-hong
Cite this article:   
ZHANG Chuan,LIU Shao-Feng,LIU Yan-Hong, et al. THE APPLICATION OF FUZZY CLASSIFICATION TO CROP CLASSIFICATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2000, 12(1): 39-43.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2000.01.08     OR     https://www.gtzyyg.com/EN/Y2000/V12/I1/39

[1] 章孝灿等.遥感数字图像处理.浙江:浙江大学出版社,1997

[2] 温熙森等.模式识别和状态监控.北京:国防科技大学出版社,1997

[3] Gong P, Howarth P. Frequency-based contextual classification and gray-level vector reduction for land use identification. Photographic Engineering and Remote Sensing, 1992, 58(4):423~437

[4] Ban Yifang, et al.. Improving the accuracy of synthetic aperture radar analysis for agriculture crop classification. Canadian Journal of Remote Sensing, 1995, 21(2):158~163

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