  The existing sonar image classification methods based on deep learning are
often analyzed in Euclidean space, only considering the local image features.
For this reason, this paper presents a sonar classification method based on
improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to
multiple types imaging sonar. This method quantifies the correlation
relationship between nodes based on the joint calculation of color proximity
and spatial proximity that represent the sonar characteristics in non-Euclidean
space, then the KNN (K-Nearest Neighbor) algorithm is used to determine the
neighborhood range and adjacency matrix in the graph attention mechanism, which
are jointly considered with the attention coefficient matrix to construct the
key part of the SI-GAT. This SI-GAT is superior to several CNN (Convolutional
Neural Network) methods based on Euclidean space through validation of real
data.
