An operational method to determine change threshold using change vector analysis

Hansong Zhang; Jianyu Chen He; Zhihua Mao

doi:10.1117/12.833001

30 October 2009 An operational method to determine change threshold using change vector analysis

Hansong Zhang, Jianyu Chen He, Zhihua Mao

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 749706 (2009) https://doi.org/10.1117/12.833001
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Digital change detection (CD) is the computerized process of identifying changes in the state of an object, or other earthsurface features, between different dates. During the last years, a large number of change detection methods have been proposed for change detection of multiple-temporal remote sensing images. Among these, change vector analysis (CVA) is a very important and widely used method. The key of CVA is to determine change detection threshold. Change detection threshold is a very valuable key for change detection precision. In the literature, many techniques to determine change detection threshold have been proposed. However, most of them are not robust and operational since images are diverse and complex, especially to very high resolution (VHR) data (e.g. images acquired by QuickBird, IKONOS, SPOT5 and WorldView satellites). Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. In this paper, we analyze the algorithm based on minimal classifying error, the algorithm based on OTSU and the algorithm based on EM. To eliminate the complexity of VHR data, an improved algorithm based on EM is proposed. Suppose the difference image meets the Mixed Gaussian distribution model. First, the grey histogram of the difference image is fitted to the Mixed Gaussian Distribution Model (MGM). Then the change detection threshold is determined by the MGM graph combing the Bayesian Criterion and the actual situation. In experiment, the semi-automatic method is effective and operational.

Citation Download Citation

Hansong Zhang, Jianyu Chen He, and Zhihua Mao "An operational method to determine change threshold using change vector analysis", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749706 (30 October 2009); https://doi.org/10.1117/12.833001

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available