Underwater Sediments Echoes Recognition Based on KECCA + PLS

In order to solve the nonlinear feature fusion of underwater sediments echoes, the shortage of Enhanced Canonical Correlation Analysis (ECCA) was analyzed and made ECCA extend to Kernel ECCA (KECCA) in the nuclear space, a multi-feature nonlinear fusion classification model with KECCA combining with Partial Least-Square (PLS ) was put forward。In the process of identifying four types of underwater sediment such as Basalt, Volcanic breccia, Cobalt crusts and Mudstone, the results showed that the recognition accuracy could be further improved for the KECCA + PLS model.