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.

翻译：基于深层学习的现有声纳图像分类方法往往在欧几里德空间进行分析,只考虑当地图像特征。因此,本文件介绍了基于改进的图形注意网络(GAT)的声纳分类方法,即适用于多种类型的成像声纳的SI-GAT。这种方法根据在非欧几里德空间中反映声纳特征的颜色相近和空间相近联合计算得出的节点之间的相互关系进行了量化,然后使用KNN(K-Nearest Neighbor)算法来确定图形注意机制中的相邻范围和相邻矩阵,并与关注系数矩阵共同审议,以构建SI-GAT的关键部分。通过验证真实数据,该SI-GAT优于基于Euclidean空间的若干CNN(进波神经网络)方法。