An image processing approach to underwater acoustic signal classification

This work focuses on the use of image processing methods to detect and classify underwater acoustic signals. The time-frequency spectra of underwater acoustic signals are usually converted to lofargrams for display purposes. These lofargrams exhibit texture-like characteristics. Moving targets exhibit ramps while ambient noise has a noisy pattern. Hence, these can be detected using textural pattern classification methods. More specifically, textural features such as contrast, entropy, inverse difference moment, etc., are computed from the co-occurrence matrices of the lofargrams. A maximum likelihood classifier is designed to classify the different patterns in the lofargrams. We have successfully classified eight different narrowband underwater acoustic signals with an average classification accuracy of 99.99%.