Remote Sensing Applications and Decision Support

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

[+] Author Affiliations
Aiye Shi, Fengchen Huang, Zhenli Ma

Hohai University, College of Computer and Information, No. 8 Focheng West Road, Jiangning District, Nanjing 211100, China

Chao Wang

Nanjing University of Information Science and Technology, School of Electronic and Information Engineering, No. 219 Ningliu Road, Pukou District, Nanjing 210044, China

Shaohong Shen

Spatial Technology Institute, Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, No. 23 Huangpu Street, Jiangan District, Wuhan 430010, China

J. Appl. Remote Sens. 10(4), 046028 (Dec 23, 2016). doi:10.1117/1.JRS.10.046028
History: Received August 10, 2016; Accepted December 2, 2016
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Abstract.  Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as “salt and pepper” noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Aiye Shi ; Chao Wang ; Shaohong Shen ; Fengchen Huang and Zhenli Ma
"Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model", J. Appl. Remote Sens. 10(4), 046028 (Dec 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046028


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