Remote sensing image change detection based on change vector analysis of PCA component

doi:10.6046/gtzyyg.2016.01.04

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Abstract

In order to monitor the change of land cover with remote sensing technology, the authors studied a method which is based on single-temporal remote sensing image in different years for extracting differences between the images and determining the change threshold automatically to extract the change area. The research took Landsat8 OLI images of Nantong City as an example. Principal component analysis (PCA) was carried out respectively on two images. After the PCA transformation, the first three components were operated based on change vector analysis (CVA) to get the difference image for change detection, which was compared with the extraction results based on the traditional PCA method and CVA method. Overall minimum error probability threshold determination method and local minimum error probability method were utilized to automatically determine the threshold of the three difference images and to get six change area images. The accuracy was evaluated by visual interpretation, and the results show that the overall accuracy of the new method can reach 92.78%, with kappa coefficient up to 0.8426. This method is proved to be feasible and effective for extracting change area by single-temporal remote sensing image in different years.

Keywords TH-1 random forests feature selection land-cover classification arid regions

TP751.1

Issue Date: 27 November 2015

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	TIAN Shaohong
	ZHANG Xianfeng

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TIAN Shaohong,ZHANG Xianfeng. Remote sensing image change detection based on change vector analysis of PCA component[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 22-27.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.01.04 OR https://www.gtzyyg.com/EN/Y2016/V28/I1/22

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