CONFERENCE PROCEEDINGS
Advanced Search
Home > Journals > J. Appl. Rem. Sens. > Volume 12 > Issue 1 > Article
20 February 2018 Bitemporal multispectral images unsupervised change detection based on undecimated wavelet transform and chi-squared transform
Aiye Shi, Shaohong Shen, Chao Wang, Zhenli Ma
Author Affiliations +
Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016025 (February 2018). https://doi.org/10.1117/1.JRS.12.016025
Abstract
Ordinary unsupervised change detection methods are mostly applied to the magnitude of spectral change vector (MSCV) of bitemporal images. However, the information contained in the different bands of the difference image is not fully exploited. We propose an unsupervised change detection method based on undecimated wavelet transform (UWT) in combination with chi-squared transform (CT). First, the UWT is applied to the difference image, and multiresolution images of each band of the difference image are obtained. Second, spatial constraint CT (SCT) is applied to the same resolution levels of each band of the difference image. Then, the results of SCT at all resolution levels are fused by a majority voting rule. After that, the fusion result as an initial solution of change detection is further improved by a Markov random field (MRF) model. In the implementation of the MRF model, the nonnegative matrix factorization is applied to fuse the MSCV and spectral angle mapper of bitemporal images to obtain the difference image. The experiments on three sets of multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2018/$25.00 © 2018 SPIE
Citation Download Citation
Aiye Shi, Shaohong Shen, Chao Wang, and Zhenli Ma "Bitemporal multispectral images unsupervised change detection based on undecimated wavelet transform and chi-squared transform," Journal of Applied Remote Sensing 12(1), 016025 (20 February 2018). https://doi.org/10.1117/1.JRS.12.016025
Received: 4 July 2017; Accepted: 25 January 2018; Published: 20 February 2018
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $24.00
Non-members: $28.00 ADD TO CART
JOURNAL ARTICLE
 17 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image fusion
Multispectral imaging
Magnetorheological finishing
Wavelet transforms
Detection and tracking algorithms
Expectation maximization algorithms
Image resolution
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top