Description: This paper presents a new automatic change de-
tection technique for synthetic aperture radar (SAR) time se-
ries, i.e., Method for generalIzed Means Ordered Series Analysis
(MIMOSA). The method compares only two different temporal
means between the amplitude images, whatever the length of the
time series. The method involves three different steps: 1) estima-
tion of the amplitude distribution parameters over the images
2) computation of the theoretical joint probability density function
between the two temporal means and 3) automatic thresholding
according to a given false alarm rate, which is the only change
detection parameter. The procedure is d with a very low
computational cost and does not require any spatial speckle fi lter-
ing. Indeed, the full image resolution is used. Due to the temporal
means, the data volume to process is reduced, which is very help-
ful.Moreover, the two means can be simply updated using the new
incoming images only. Thus, the fu
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