Land cover information extraction from remote sensing images using object-based image analysis method integrated with decision tree

doi:10.6046/gtzyyg.2016.04.24

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Abstract

Object-based image analysis, which has been developed rapidly over the last decades, performs advantageous over classic pixel-based image classification. One of the key problems within this paradigm is to automatically build robust and transferable rule sets for segment classification. It has been identified promisingly to develop rule sets by means of decision tree based on data mining. The authors suggest a decision tree model integrated with J48 algorithm embedded in Weka to select parameters from a set of spectral, textural and terrain features relevant to rule sets for segment classification. Based on this method, the authors used Landsat5 TM image data and ASTER digital elevation model to establish land cover classification in the study area, i.e., Baicaoyuan area in Huining county, Gansu Province. Rule sets developed in this way perform acceptable robustness and transferability. Accuracy assessment proves that this method has significantly higher classification accuracy than other pixel-based methods based on employing maximum likelihood and objected-based nearest neighbor logic.

Keywords atmospheric correction FLAASH model 6S model suspended sediment concentration(SSC) remote sensing retrieval

TP79

Issue Date: 20 October 2016

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	KONG Jinling
	YANG Jing
	SUN Xiaoming
	YANG Shu
	LIU Futian
	DU Dong

Cite this article:

KONG Jinling,YANG Jing,SUN Xiaoming, et al. Land cover information extraction from remote sensing images using object-based image analysis method integrated with decision tree[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 156-163.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.04.24 OR https://www.gtzyyg.com/EN/Y2016/V28/I4/156

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