I am quoting the following paragraph from Preiss and Stacy 2006:
The mean backscatter power ratio is sensitive to changes in the average backscattered energy in the transduced imagery. The sample coherence on the other hand is sensitive to changes in the speckle noise pattern in the repeat pass image pair. Scene disturbances arising from man-made changes however, can potentially cause changes over a broad range of scattering properties. In  the sample coherence and mean backscatter power ratio were used to detect changes in repeat pass ERS-1 SAR imagery. It was found that the areas of disturbance identiﬁed by each method did not necessarily agree and each method gives complementary characterisations of scene changes. Therefore both change statistics should be considered to provide a complete description of scene changes. In the context of change detection this presents problems in fusing the detections from the two change statistics to achieve a single combined detection list in which the probability of detection is maximised whilst minimising the false alarm rate.
An alternative approach to discriminating between those regions aﬀected by man-made scene changes and those that are not can be achieved by formulating the detection problem in an hypothesis tesing framework. In this approach the change detection problem is to determine whether pixel pairs Xk = [fk,gk]T,k = 1···N in a local area are realisations of a null (unchanged scene) hypothesis H0 or an alternative (changed scene) hypothesis H1...
Given a local neighbourhood of N independent pixels Xk = [fk,gk]T,k = 1···N a simple decision statistic for determining whether the pixels are realisations of the unchanged hypothesis or changed hypothesis is the likelihood ratio deﬁned as,
The end is based in two hypothesis...
I was processing a data set from ALOS PALSAR 1 with three methods for change detection:
1.- Composition multitemporal in RGB,
2.- Log Ratio,