Combining acoustic and visual survey data through multimodal machine learning for wide area seafloor habitat classification

通过多模式机器学习结合声学和视觉调查数据进行广域海底栖息地分类

• 批准号: 2570102
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2570102
• 关键词: Combining; acoustic; visual; survey; data

Autonomous underwater vehicles (AUVs) are increasingly used for a wide range of marine surveys, for example to support marine spatial planning, conservation or monitoring. Recently, robotic camera surveys have begun to complement wide-area acoustic mapping efforts, as visual images can provide more detailed information that can be used for more reliable classification of seafloor habitats than acoustic data alone. However, the strong attenuation of light in water requires visual data to be gathered at close range, limiting the areas that can be observed. This tradeoff between survey extent and detail remains an open challenge in marine survey. The aim of this project is to develop novel objective approaches to build probabilistic habitat models that learn relationships between habitat classes derived from visual imagery and infer these onto wide-area environmental (primarily acoustic) data. The new techniques will be applied to extensive datasets of visual seabed images, typically collected by AUVs in benthic environments of conservation interest (e.g. cold-water coral reefs, submarine canyons,..). This work will establish best practice to estimate seafloor habitat distribution at the regional scales needed for marine conservation and monitoring.

自主水下航行器越来越多地用于各种海洋调查，例如支持海洋空间规划、养护或监测。最近，机器人摄像机勘测已开始补充大面积声学测绘工作，因为视觉图像可以提供比单独的声学数据更详细的信息，可用于更可靠的海底生境分类。然而，光在水中的强烈衰减要求在近距离收集视觉数据，限制了可以观察的区域。在海洋调查中，这种调查范围和细节之间的权衡仍然是一个公开的挑战。该项目的目的是开发新的客观的方法来建立概率栖息地模型，学习来自视觉图像的栖息地类之间的关系，并推断这些到广域环境（主要是声学）数据。这些新技术将应用于通常由水下机器人在具有养护意义的海底环境（例如冷水珊瑚礁、海底峡谷等）中收集的大量可视海底图像数据集。这项工作将确立最佳做法，以估计海洋养护和监测所需的区域海底生境分布。