An Improved MRF Based Unsupervised Change Detection Method
作者： Young-bok Cho , Yong-zhen Li , Ning Sun , Sang-Ho Lee
DOI: 10.1109/CIS.2007.100
关键词:
摘要: Traditional unsupervised change detection algorithms based on simple MRF model assume that subimages applied to extracting features are homogeneous, but is not always true and causes low accuracy. Based the fields correlation Markov random field (CMRF) model, an adaptive algorithm proposed in this paper. The labeling obtained through solving a MAP (Maximum posterior) problem by ICM (Iteration Condition Model). Features of each pixel exacted using only pixels currently labeled as same pattern. With adapted features, new obtained. satisfied experimental confirm effectiveness techniques. Although method has been presented specific context analysis multitemporal remote-sensing images, it could be used any application requiring technique difference image
sci-hub.se 参考文章(17)
Daniel C. Nash, An Intrusion Detection System for Battery Exhaustion Attacks on Mobile Computers Virginia Tech. ,(2005)
政清 宮沢, P. Bremaud 著, Markov Chains, (Gibbs fields, Monte Carlo simulation and Queues), Springer-Verlag, 1999年 オペレーションズ・リサーチ : 経営の科学. ,vol. 45, pp. 85- 86 ,(2000)
Rafael Wiemker, An Iterative Spectral-Spatial Bayesian Labeling Approach for Unsupervised Robust Change Detection on Remotely Sensed Multispectral Imagery computer analysis of images and patterns. pp. 263- 270 ,(1997) , 10.1007/3-540-63460-6_126
Allan A Nielsen, Knut Conradsen, James J Simpson, None, Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies Remote Sensing of Environment. ,vol. 64, pp. 1- 19 ,(1998) , 10.1016/S0034-4257(97)00162-4
Qiang Wu, M. Martonosi, D.W. Clark, V.J. Reddi, D. Connors, Youfeng Wu, Jin Lee, D. Brooks, Dynamic-Compiler-Driven Control for Microprocessor Energy and Performance IEEE Micro. ,vol. 26, pp. 119- 129 ,(2006) , 10.1109/MM.2006.9
Jianchang Mao, Anil K. Jain, Texture classification and segmentation using multiresolution simultaneous autoregressive models Pattern Recognition. ,vol. 25, pp. 173- 188 ,(1992) , 10.1016/0031-3203(92)90099-5
S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, Optimization by Simulated Annealing Science. ,vol. 220, pp. 671- 680 ,(1983) , 10.1126/SCIENCE.220.4598.671
J IM, J JENSEN, A change detection model based on neighborhood correlation image analysis and decision tree classification Remote Sensing of Environment. ,vol. 99, pp. 326- 340 ,(2005) , 10.1016/J.RSE.2005.09.008
Gabriele Moser, Unsupervised change-detection methods for remote-sensing images Optical Engineering. ,vol. 41, pp. 3288- 3297 ,(2002) , 10.1117/1.1518995
T.K. Buennemeyer, G.A. Jacoby, W.G. Chiang, R.C. Marchany, J.G. Tront, Battery-Sensing Intrusion Protection System 2006 IEEE Information Assurance Workshop. pp. 176- 183 ,(2006) , 10.1109/IAW.2006.1652093