USING THE MODEL OF SELF-ORGANIZED NEURAL TREE TO THE CLASSIFICATION OF FORESTLANDS

doi:10.6046/gtzyyg.1997.03.08

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Abstract Through the information of forestlands obtained from remote sensing pictures of 692 land patches, this paper has studied the forestlands classification in the method of self-organizing neural tree model under the support of GIS. The results show that this method of classification has so many advantages, such as speed, accuracy and tolerence.

Keywords Radarsat SAR Forest stock volume estimation Tree height Diameter at breast height Backscatter coefficient

Issue Date: 02 August 2011

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	Articles by authors

	WANG Chen-Li
	NIU Zheng
	GUO Zhi-Xin
	CONG Pi-Fu
	DENG Xiao-Lian
	FENG Lin-Gang
	ZHANG Suo-Xiang
	MENG Kui-Wen

Cite this article:

WANG Chen-Li,NIU Zheng,GUO Zhi-Xin, et al. USING THE MODEL OF SELF-ORGANIZED NEURAL TREE TO THE CLASSIFICATION OF FORESTLANDS[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(3): 46-50.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1997.03.08 OR https://www.gtzyyg.com/EN/Y1997/V9/I3/46

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