Species identification using wideband backscatter with neural network and discriminant analysis

The paper reports the results of species-recognition-rate measurements on caged aggregations of mackerel, horse mackerel, saithe, haddock, and two sizes of cod. Data on the acoustic backscattering coefficients were collected in eight contiguous bandwidth intervals covering the frequency band between 27 and 54 kHz. The measurements were made during two to six periods of 24 h for each aggregation of fish. Replicate experiments were carried out for mackerel, horse mackerel, and two sizes of cod. The data were processed to give average frequency spectra. The number of independent observations used to establish the mean was varied to examine the species-recognition dependence on the number of independent observations. The mean spectra were analysed using two recognition methods: neural network and discriminant analysis. A neural network was trained on subsets of the data and recognition rates established for the different numbers of samples used to calculate the mean spectra. Classical discriminant analysis was applied using the same data sets. The results of the two identification methods are presented and show that recognition rates of about 95% are possible using average spectra. The differing recognition rates by species and fish sizes are discussed and the two identification methods compared. Implications for the future development of these methods are considered.