An improved CVAPS method for automatic updating of LUCC classification

doi:10.6046/gtzyyg.2018.02.04

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Abstract

The method of change vector analysis in posterior probability space(CVAPS) does not take into consideration the correlation between the bands of remote sensing image, which may result in unreliable change detection. In view of such a situation, the authors introduced multivariate change detection(MAD)method and, in combination with CVAPS, proposed an improved method for automatic updating of land use / cover change(LUCC) classification. The method firstly introduces MAD to reduce bands-correlation for improving the reliability of train-samples and accordingly improving LUCC updating maps, and then included an iterative Markov random field(IR-MRF)model to fully employ the contextual information in post-processing to reduce the noise of “salt-and-pepper”. Choosing Changting County of Fujian Province as the study area, the authors used Landsat5 TM and Landsat8 OLI data acquired in 2003 and 2013 respectively, and took OLI as the base image to update the classification map in 2003. The experimental results show that the proposed method significantly outperforms the CVAPS in that its overall accuracy could reach 80% with the improvement rate being about 3%.

Keywords land use/cover change(LUCC) automatic updating multivariate change detection(MAD) change vector analysis in posterior probability space(CVAPS) iterative Markov random field(IR-MRF)model

TP751.1

Corresponding Authors: Bo WU E-mail: 1161336850@qq.com;wavelet778@sohu.com

Issue Date: 30 May 2018

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	Xinran ZHU
	Bo WU
	Qiang ZHANG

Cite this article:

Xinran ZHU,Bo WU,Qiang ZHANG. An improved CVAPS method for automatic updating of LUCC classification[J]. Remote Sensing for Land & Resources, 2018, 30(2): 29-37.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.04 OR https://www.gtzyyg.com/EN/Y2018/V30/I2/29

Fig.1 Flow chart for automatic updating of LUCC

Fig.2 Original remote sensing images and references classification map of study area

Fig.3 Automatic updating of LUCC classification results

Tab.2 Confusion matrix of CVAPS classification

Tab.3 Comfusion matrix of classification using method proposed in this paper

Fig.4 Comparison among three kinds of change detection results

Tab.4 Results of changed/ unchanged pixels detected by MAD

Tab.5 Results of changed/ unchanged pixels detected by CVAPS

Tab.6 Results of changed/ unchanged pixels detected by method proposed in this paper

Fig.5 Curves of iterative times and consistency ratio

Fig.6 Result of automatic updating and classification improved by IR-MRF model

Fig.7 Histogram for precision comparison between IR-MRF model used and unused

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