Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification

doi:10.6046/gtzyyg.2014.04.06

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Abstract

To solve the change detection problem of multi-channel remote sensing images, this paper proposes a method based on iterative estimation with weight selection (IEWS) and unsupervised classification (UC). Firstly, the primary change information is obtained according to the concept of IEWS, and the iteration scheme of the estimation is also similar to that of the iteratively re-weighted multivariate alteration detection (IRMAD). And then, the primary change information is classified by the UC and processed by the IEWS, which can get the eventual change information. The experimental results with multi-spectral data indicate that the method proposed in this paper is effective. By using this method, the spatial coherence between the change information and the change of land use/cover in this area is good. As for the detection of change in small regions, the method is especially obviouely better than the commonly-used methods of multivariate alteration detection (MAD) and IRMAD.

Keywords sea ice classification synthetic aperture Radar(SAR) ice concentration

TP751.1

Issue Date: 17 September 2014

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	LIU Huiying
	GUO Huadong
	ZHANG Lu

Cite this article:

LIU Huiying,GUO Huadong,ZHANG Lu. Change detection of multi-spectral images based on iterative estimation with weight selection and unsupervised classification[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 34-40.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.04.06 OR https://www.gtzyyg.com/EN/Y2014/V26/I4/34

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