THE APPLICATION OF FUZZY CLASSIFICATION TO CROP CLASSIFICATION

doi:10.6046/gtzyyg.2000.01.08

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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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