Spectrogram analysis of acoustical sounds for underwater target classification is utilized when loud nonstationary interference sources overlap with a signal of interest in time but can be separated in time-frequency ( ) domain. We propose a signal masking method which in a plane combines local statistical and morphological features of the signal of interest. A dissimilarity measure of adjacent cells is used for local estimation of entropy , followed by estimation of entropy difference, where is calculated along the time axis at a mean frequency and is calculated along the frequency axis at a mean time of the window, respectively. Due to a limited number of points used in estimation, the number of possible values, which define a primary mask, is also limited. secondary mask is defined using morphological operators applied to, for example, and . We demonstrate how primary and secondary masks can be used for signal detection and discrimination, respectively. We also show that the proposed approach can be generalized within the framework of Genetic Programming.
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