论文标题
部分可观测时空混沌系统的无模型预测
SI-GAT: A method based on improved Graph Attention Network for sonar image classification
论文作者
论文摘要
经常在欧几里得空间中分析基于深度学习的现有声纳图像分类方法，仅考虑局部图像特征。因此，本文提出了一种基于改进的图形注意网络（GAT）的声纳分类方法，即Si-Gat，该方法适用于多种类型的成像声纳。 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.通过验证真实数据，该SI-GAT优于基于欧几里得空间的几种CNN（卷积神经网络）方法。
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.
