The task of detection of objects composed of several regions by means of statistical filters is analyzed. The target is assumed to have different unknown mean values in each of its regions. The detection is based on likelihood estimation, after performing an estimation of the actual configuration of the mean values in the target region. A simplified filter that reduces the computational complexity is also proposed. The statistical performance is analyzed theoretically and tested in computer experiments.