Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA)—where data from an alternate region, known as the source domain, are used to train a classifier and this model is adapted to map the study region, or target domain. The scenario we address in this paper is known as semi-supervised DA, where some labelled samples are available in the target domain. In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from satellite image time series (SITS) data. The technique takes a convolutional neural network trained on a source domain and then trains further on the available target domain with a novel regularizer applied to the model weights. The regularizer adjusts the degree to which the model is modified to fit the target data, limiting the degree of change when the target data are few in number and increasing it as target data quantity increases. Our experiments on Sentinel-2 time series images compare Sourcerer with two state-of-the-art semi-supervised domain adaptation techniques and four baseline models. We show that on two different source-target domain pairings Sourcerer outperforms all other methods for any quantity of labelled target data available. In fact, the results on the more difficult target domain show that the starting accuracy of Sourcerer (when no labelled target data are available), 74.2%, is greater than the next-best state-of-the-art method trained on 20,000 labelled target instances.

土地覆盖图是许多类型的环境研究与管理的重要输入变量。尽管可以通过机器学习技术自动生成它们，但是这些技术需要大量的训练数据才能达到较高的准确性，而这些准确性并非总是可用。研究人员在缺乏标记的训练数据时使用的一种技术是域适应（DA），其中来自替代区域的数据（称为源域）用于训练分类器，并且对该模型进行了适应绘制研究区域或目标领域。我们在本文中讨论的场景称为半监督DA，其中在目标域中有一些带标记的样本。在本文中，我们介绍Sourcerer，这是一种基于贝叶斯的，基于深度学习的半监督DA技术，用于根据卫星图像时间序列（SITS）数据生成土地覆盖图。该技术采用在源域上训练的卷积神经网络，然后使用适用于模型权重的新型正则器在可用目标域上进行进一步训练。正则化器调整修改模型的程度以适合目标数据，当目标数据数量很少时限制更改的程度，并随着目标数据量的增加而增加。我们在Sentinel-2时间序列图像上进行的实验将Sourcerer与两种最新的半监督域自适应技术和四种基线模型进行了比较。我们显示，在任意数量的可用标记目标数据上，Sourcerer在两个不同的源－目标域配对上均胜过所有其他方法。实际上，在较困难的目标域上的结果显示，Sourcerer的起始准确度（当没有可用的标记目标数据时）为74.2％，比在20,000个标记的目标上训练的第二好的方法要高。目标实例。