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.

本文提出了一种基于改进图形注意力网络 (SI-GAT) 的声纳分类方法，该方法可以在多种类型成像声纳中应用，并通过实际数据验证优于几种以欧几里得空间为基础的卷积神经网络方法。

SI-GAT: 改良图注意力网络方法用于声纳图像分类

SI-GAT: A method based on improved Graph Attention Network for sonar image classification

Self-supervised learning has proved to be a powerful approach to learn image
representations without the need of large labeled datasets. For underwater
robotics, it is of great interest to design computer vision algorithms to
improve perception capabilities such as sonar image classification. Due to the
confidential nature of sonar imaging and the difficulty to interpret sonar
images, it is challenging to create public large labeled sonar datasets to
train supervised learning algorithms. In this work, we investigate the
potential of three self-supervised learning methods (RotNet, Denoising
Autoencoders, and Jigsaw) to learn high-quality sonar image representation
without the need of human labels. We present pre-training and transfer learning
results on real-life sonar image datasets. Our results indicate that
self-supervised pre-training yields classification performance comparable to
supervised pre-training in a few-shot transfer learning setup across all three
methods. Code and self-supervised pre-trained models are be available at
this https URL

本研究探讨了三种自监督学习方法（RotNet，去噪自编码器和 Jigsaw）在水下机器人领域中，能否在无需大型标记数据集的情况下学习高质量的声纳图像表示，并提出了预训练和迁移学习的结果。结果表明，三种方法的自监督预训练可在少数样本迁移学习设置中实现与有监督预训练相当的分类性能。