Remote sensing classification method based on image segment spatial relationship

doi:10.6046/gtzyyg.2013.01.14

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Abstract

An image classification method based on the spatial relationship of image segment is proposed with the purpose of excavating the spatial relationship between image segments and compensating for deficiencies of the traditional image classification method based on spectral information. Image segmentation is used to get image segments for original image classification employing maximum likelihood (ML) method. Then the spatial relationship of image segments is described by Markov random field (MRF). Quantitative spatial relationship can be obtained by class adjacency matrix (CAM) so as to revise the result of classification. After that the iterated conditional mode (ICM) algorism for classification is presented, which can yield results with higher accuracy. Experimental results show that this method has been functioning well in classification experiments with high resolution remote sensing images.

Keywords soil hyper-spectrum remote sensing potassium

TP751.1

Issue Date: 21 February 2013

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	Articles by authors

	HU Fang
	LIN Qi-zhong
	WANG Qin-jun
	WANG Ya-jun

Cite this article:

HU Fang,LIN Qi-zhong,WANG Qin-jun, et al. Remote sensing classification method based on image segment spatial relationship[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 77-81.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.01.14 OR https://www.gtzyyg.com/EN/Y2013/V25/I1/77

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