多波束声呐海底底质半监督学习分类方法

多波束测深声呐的反向散射数据中包含海底表层的声学信息，可以用来进行海底表层底质分类。但实际中通过物理采样获得大范围的底质类型的标签信息所需成本过高，制约了传统监督分类算法的性能。针对实际应用中只拥有大量无标签数据和少量有标签数据的情况，文章提出了基于自动编码器预训练以及伪标签自训练的半监督学习底质分类算法。利用2018年和2019年两次同一海域实验采集的多波束测深声呐反向散射数据，对所提算法进行了验证。数据处理结果表明，相比仅利用有标签数据的监督分类算法，提出的半监督学习分类算法保证分类准确率的同时所需的有标签数据更少。自动编码器预训练的半监督学习分类方法在有标签样本数量极少时的准确率仍高于75%。

Semi-supervised learning methods for seafloor sediment classification using multi-beam sonar

The backscatter data from multi-beam sonar contain acoustic information on the seafloor surface, which can be used to classify the seafloor. However, in practice, the cost of obtaining label information of a wide range of seafloor sediment types through physical sampling is too high, which restricts the performances of traditional supervised classification algorithms. In response to this problem, two semi-supervised learning classification algorithms based on auto-encoder pre-training and pseudo-label self-training are proposed, which can be used in the situation where there are only a large amount of unlabeled data and a small amount of labeled data in practical applications. The proposed algorithms are validated by using the multi-beam sonar backscatter data collected from two experiments in the same sea area in 2018 and 2019. Data processing results show that the proposed algorithms require less labeled data while ensuring classification accuracy, compared to the supervised classification algorithms only using labeled data. The classification accuracy of the semi-supervised learning classification method pre-trained by autoencoders is still above 75% when the number of labeled samples is very small.