Paper Info
Title
A new model-independent method for change detection in multitemporal SAR images based on Radon transform and Jeffrey divergence
Abstract
This letter presents a new approach for change detection in multitemporal synthetic aperture radar images. Considering about the existence of speckle noise, the local statistics in a sliding window are compared instead of pixel-by-pixel comparison. Edgeworth series expansion is applied to estimate the probability density function (pdf), which is on the assumption that the pdf is not too far from normal distribution. To transcend such a limitation, in each analysis window, the image is projected onto two vectors in two independent dimensions; thus, the pdf of each projection is closer to a Gaussian density. In order to measure the distance between the two pairs of projections, the proposed algorithm uses a modified Kullback-Leibler (KL) divergence, called Jeffrey divergence, which turns out to be more numerically stable than KL divergence. Experiments on the real data show that the proposed detector outperforms all the others when a high detection rate is demanded. © 2012 IEEE.
Year
DOI
Venue
2013
10.1109/LGRS.2012.2193659
Geoscience and Remote Sensing Letters, IEEE
Keywords
DocType
Volume
change detection,edgeworth series expansion,jeffrey divergence,radon transform,synthetic aperture radar (sar) images,detectors,histograms,probability density function,remote sensing
Journal
10
Issue
ISSN
Citations 
1
null
7
PageRank 
References 
Authors
0.49
8
2
Name
Order
Citations
PageRank
Jin Zheng1464.22
Hongjian You210317.44