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

自主水下航行器(AUV)越来越多地用于广泛的海洋调查，例如支持海洋空间规划、养护或监测。最近，机器人相机勘测已开始补充广域声学测绘工作，因为视觉图像可以提供比仅有声学数据更可靠的海底生境分类所需的更详细的信息。然而，光在水中的强烈衰减要求近距离收集视觉数据，这限制了可以观察到的区域。在海洋调查中，在调查范围和细节之间进行权衡仍然是一个悬而未决的挑战。这个项目的目的是开发新的客观方法来建立概率生境模型，该模型学习来自视觉图像的生境类别之间的关系，并将这些关系推断到广域环境(主要是声学)数据上。新技术将应用于可视海底图像的大量数据集，通常由水下机器人在具有保护意义的海底环境(例如冷水珊瑚礁、海底峡谷等)收集。这项工作将确立最佳做法，估计海洋养护和监测所需的区域范围内的海底栖息地分布。

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.