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Multitemporal interferometric synthetic aperture radar (MTI) displacement time series are usually characterized by the model-dependent temporal phase coherence as a quality measure. Additional tests have to be performed to recognize other “interesting” but nonmodeled trends, and several automated approaches to this task have been proposed to date. We introduce here the fuzzy entropy ($F_{E}$), a measure introduced in medical data analysis, as a viable parameter to characterize MTI time series. Being a measure of disorder in a time series, $F_{E}$ exhibits homogeneously low values for a large class of displacement models, such as seasonal, parabolic, or piecewise linear signals, while increasing for more chaotic trends, dominated by noise. It appears therefore suited as a discriminative parameter to isolate meaningful MTI time series within large data sets, without specifying a predefined model. The calculation of $F_{E}$ has low computational cost and can thus be easily performed as a prescreening filter. In this letter, results over simulated data and some examples on a real data set are shown with interesting performances which hint to possible large-scale implementations. [ABSTRACT FROM AUTHOR]