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Abstract-Signal detection of primary users for cognitive radios enables spectrum use agility. In normal operation conditions, the sensed spectrum is nonflat i.e. the power spectrum is not constant. A novel method proposed the segmentation of the measured spectra into regions where the flatness condition is approximately valid. As a result, an automatic detection of the significant spectral components together with an estimate of the magnitude of the spectral component and a measure of the quality of classification became available. In this paper, we optimize the methodology for signal detection for cognitive radios such that the probability that a spectral component was incorrectly classified is minimized. Simulation and measurement results show the advantages of the presented technique in different types of measured spectra.