  This paper discusses change detection in SAR time-series. Firstly, several
statistical properties of the coefficient of variation highlight its pertinence
for change detection. Then several criteria are proposed. The coefficient of
variation is suggested to detect any kind of change.
  Then other criteria based on ratios of coefficients of variations are
proposed to detect long events such as construction test sites, or point-event
such as vehicles.
  These detection methods are evaluated first on theoretical statistical
simulations to determine the scenarios where they can deliver the best results.
Then detection performance is assessed on real data for different types of
scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative
evaluation is performed with a comparison of our solutions with
state-of-the-art methods.
