Change detection algorithm on wavelet and markov random field

被引：0

作者：

Hongxun, Song ^{[1
,2
]}

Weixing, Wang ^{[1
,2
]}

Tingting, Zhang ^{[1
,2
]}

Tianchao, Yu ^{[1
,2
]}

Junfang, Song ^{[1
,2
]}

机构：

[1] Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center, Chang’an University, Xi’an, China

[2] School of Information Engineering, Chang’an University, Xi’an, China

来源：

International Journal of Signal Processing, Image Processing and Pattern Recognition | 2015年 / 8卷 / 04期

关键词：

Image segmentation - Markov processes - Wavelet transforms - Remote sensing - Signal detection - Image fusion;

D O I：

10.14257/ijsip.2015.8.4.16

中图分类号：

学科分类号：

摘要：

In this study, the algorithm that applies Wavelet and multi-scale analysis to remote sensing images is proposed for region variation detection on Markov random field. First of all, the Wavelet transform is adopted to decompose an original image into several sub-images, then the Mahalanobis distance decision function is used to detect the changes in different scale images, and finally the Markov random field is applied to fuse the change detection results at different scales. Since the Markov random field fusion method takes full account of the correlation between the adjacent pixels and the links of the change detection results at different scales, the fusion results are accurate and practical. The testing results prove that the studied algorithm is effective and robust. © 2015 SERSC.

引用

页码：181 / 190