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This paper addresses the problem of matching the statistical properties of the distributions of two (or more) multi-spectral remote sensing images acquired on the same geographical area at different times. An N-D probability density function (pdf) matching technique for the preprocessing of multitemporal images is introduced in the remote sensing domain by defining and analyzing three important application scenarios: 1) supervised classification; 2) partially supervised classification; and 3) change detection. Unlike other methods adopted in remote sensing applications, the procedure considered performs the matching process by properly taking into account the correlation among spectral channels, thus retaining the data correlation structure after the pdf matching. Experimental results obtained on real multitemporal remote sensing data sets confirm the validity of the presented technique in all the considered scenarios.