Bayesian Meta-Learning for Earth Observation: Better Models with Less Data

在过去的十年里，机器学习给模式识别系统带来了显著的改进[5]。这一成功归因于深度神经网络的使用、非常大的注释训练数据集和丰富的计算资源。然而，对于许多地球观测问题，只有很小的训练数据集可用。处理有限训练数据的一个很有前途的机器学习技术家族是元学习--也被称为学习学习[4]。给定来自同一应用领域的几个模式识别问题(例如，卫星图像中的目标检测)，这些方法能够自动构建专用于感兴趣的应用领域的新的机器学习算法。与传统的机器学习方法相比，这些元学习算法在同一领域的新任务上部署时，需要更少的数据和计算资源来训练有效的模型。元学习方法最常应用于包含人或日常对象的图像的数据集，目标是创建能够在包含新对象类别的图像上快速训练模型的学习算法。这种方法不适合于对地观测问题的设置，因为与常规的日常照片收集相比，对地观测数据具有额外的结构。此外，在许多情况下，卫星将获取同一地区的多幅图像，这些图像之间的内容将有相当大的重叠。这种重叠的内容违反了机器学习方法所做的标准独立性假设。天真地忽视这种结构将产生性能不佳的机器学习模型，但定制的元学习方法如果明确利用数据中的这些时间和空间结构，可能会提供更强大的模型。该项目的目标是开发一个适合用于遥感应用的元学习框架，从而使科学家能够在不需要昂贵的数据注释过程或机器学习专业知识的情况下快速训练准确的模型。元学习方法将利用卫星成像系统通常捕获的多种图像模式(例如，RGB、红外、多光谱/高光谱、距离/激光雷达等)，并利用收集数据集的地理区域(例如，纬度和经度、历史天气模式、人口密度等)以及不同感兴趣区域在同一数据集中的相对位置的附加背景信息。通过将深度学习与新的贝叶斯预测头相结合，该框架将能够利用Well[1]校准的不确定性估计所提供的所有好处。这种好处的例子包括容易地检测异常[2]，能够将来自其他来源的信息与从卫星图像提取的信息融合，以及主动学习[1]以进一步最有效地利用有限的标签注释资源。

Machine learning has brought significant improvements to pattern recognition systems in the past decade [5]. The three main factors that this success is attributed to are the use of deep neural networks, very large annotated training datasets, and abundant computing resources. However, for many earth observations problems only small training datasets are available. One promising family of machine learning techniques for dealing with limited training data is meta-learning-also known as learning to learn [4]. Given several pattern recognition problems from the same application domain (e.g., object detection in satellite images) these approaches are able to automatically construct new machine learning algorithms that are specialised for the application domain of interest. When deployed on novel tasks from the same domain, these meta-learned algorithms require less data and computing resources in order to train an effective model compared to conventional machine learning approaches.Meta-learning methods are most commonly applied to datasets containing images of people or everyday objects, with the goal of creating learning algorithms that are able to quickly train models on images that contain novel object categories. Such methods are unsuitable for the earth observation problem setting, because of the additional structure in earth observation data compared to conventional everyday photo collections. In addition, in many situations satellites will acquire multiple images of the same region, and there will be considerable overlap in content between these images. This overlapping content violates standard independence assumptions made by machine learning methods. Naively ignoring this structure will yield machine learning models with suboptimal performance, but bespoke meta-learning approaches that instead explicitly take advantage of these temporal and spatial structures in the data have the potential to provide even more powerful models.The objective of this project is to develop a meta-learning framework that is suitable for use with remote sensing applications, thus enabling scientists working with earth observation data to rapidly train accurate models without costly data annotation processes or machine learning expertise. The meta-learning approaches will take advantage of the multiple image modalities commonly captured by satellite imaging systems (e.g., RGB, infrared, multispectral/hyperspectral, range/LiDAR, etc), and leverage additional contextual information of the geographic areas in which datasets are gathered (e.g., latitude and longitude, historical weather patterns, population density, etc) and the relative position of different regions of interest within the same dataset. By combining deep learning with novel Bayesian prediction heads, the framework will be able to take advantage of all the benefits provided by well[1]calibrated uncertainty estimates. Examples of such benefits include easily detecting anomalies [2], being able to fuse information from other sources with the information extracted from satellite images, and active learning [1] to further make the most effective use of limited label annotation resources.