Classifying sidescan sonar images using self organizing maps

Abstract

Self organizing maps (SOMs) can be used for computer-aided classification of objects found in two-dimensional snippets of sidescan sonar images. SOMs are briefly discussed, including the choice of features or attributes as well as various types of input data. The inputs can be, for example, the data values themselves, either raw or processed images; the amplitudes of the Fourier transform coefficients of the data; the wavelet transform coefficients of the data; the energies of the horizontal, vertical, or diagonal wavelet coefficients; the autocorrelation of the data; the Hartley transform coefficients of the data; the cepstrum; the dimensions of the object; or the sonar bright spot and shadow character. Tabular results and two-dimensional maps showing the groupings of measured and processed sidescan data are presented. Comparisons are made with human classifications of the same images. [Research supported in part by NRL-ASEE Summer Faculty Research Program.]