用于海洋内孤立波定位的尺度-平移等变网络
Scale-Translation Equivariant Network for Oceanic Internal Solitary Wave Localization
AI总结：
针对海洋内孤立波自动定位中高度计数据分辨率低、标注数据有限的问题，提出结合尺度-平移等变卷积神经网络与SimCLR预训练的方案，引入对称性与无监督数据先验，在自制数据集上性能优于基线模型。
摘要：
内孤立波（ISWs）是一种重力波，通常观测于海洋内部而非海面。这类波因能够携带大量能量而具有重要意义，会影响污染物输运、石油平台作业、潜艇导航等。研究人员已通过光学图像、合成孔径雷达（SAR）图像以及遥感仪器的高度计数据开展内孤立波研究。然而，光学遥感图像中的云层会不同程度地遮挡地表信息，导致海面观测模糊或缺失。因此，本文旨在研究基于高度计数据的机器学习解决方案，以实现内孤立波的自动定位。但该研究面临两方面挑战：1）高度计数据分辨率较低，需要性能强大的机器学习模型；2）数据标注极为耗费人力，导致可用训练数据十分有限。近年来，深度学习的长足发展展现出在数据充足情况下的强大学习能力；此外，近期关于高效学习与自监督学习的研究也为应对上述挑战奠定了坚实基础。本文提出引入先验知识以构建性能强且效率高的学习模型。具体而言，我们采用尺度-平移等变卷积神经网络（ST-ECNN）来高效捕捉高度计数据中的固有模式。通过在神经网络设计中纳入内在对称性考量，ST-ECNN相比基线模型实现了更高的效率与更优的性能。此外，我们还引入来自海量无监督数据的先验知识，采用SimCLR框架进行预训练以增强方案效果。我们的最终方案在自制的高度计数据集上整体性能优于各基线模型。数据与代码可访问https://github.com/ZhangWan-byte/Internal_Solitary_Wave_Localization 获取。
英文摘要：
Internal solitary waves (ISWs) are gravity waves that are often observed in the interior ocean rather than the surface. They hold significant importance due to their capacity to carry substantial energy, thus influence pollutant transport, oil platform operations, submarine navigation, etc. Researchers have studied ISWs through optical images, synthetic aperture radar (SAR) images, and altimeter data from remote sensing instruments. However, cloud cover in optical remote sensing images variably obscures ground information, leading to blurred or missing surface observations. As such, this paper aims at altimeter-based machine learning solutions to automatically locate ISWs. The challenges, however, lie in the following two aspects: 1) the altimeter data has low resolution, which requires a strong machine learner; 2) labeling data is extremely labor-intensive, leading to very limited data for training. In recent years, the grand progress of deep learning demonstrates strong learning capacity given abundant data. Besides, more recent studies on efficient learning and self-supervised learning laid solid foundations to tackle the aforementioned challenges. In this paper, we propose to inject prior knowledge to achieve a strong and efficient learner. Specifically, intrinsic patterns in altimetry data are efficiently captured using a scale-translation equivariant convolutional neural network (ST-ECNN). By considering inherent symmetries in neural network design, ST-ECNN achieves higher efficiency and better performance than baseline models. Furthermore, we also introduce prior knowledge from massive unsupervised data to enhance our solution using the SimCLR framework for pre-training. Our final solution achieves an overall better performance than baselines on our handcrafted altimetry dataset. Data and codes are available at https://github.com/ZhangWan-byte/Internal_Solitary_Wave_Localization .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
