The aim of this paper is to present a general framework for change detection in a time series of radar images, for an operational purpose and in the context of environmental monitoring. The change detection procedure is turned into the framework of detecting a random signal into the noise; the detection of this signal leads to the detection of a change in the time series. This framework is based on a non-parametric detection method that assume a sparse representation of the data. When using

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... r images, the speckle noise invalidates the hypothesis of sparsity. Then a pre-processing technique is required to provide highly sparsifiable representation of data, whatever the initial noise characteristics. The paper focuses on the change indicator, based on recursive median filtering, yielding a piecewise regular representation of a scene obtained by spreading the statistically most reliable pixel values over the image. The recursive median filtering leads to simple change indicators that are more efficient than the Kullback-Leibler change indicator when using small analysing sliding window. Furthermore, it induces an simple extension to perform progressive change characterization through a multitemporal filtering approach. Results are shown with a two-date change detection from RADARSAT images and from a time series of ERS and ENVISAT images.