This paper proposes a novel domain adaptation algorithm to handle the challenges posed by the satellite and aerial imagery, and demonstrates its effectiveness on the built-up region segmentation problem. Built-up area estimation is an important component in understanding the human impact on the environment, effect of public policy and in general urban population analysis. The diverse nature of aerial and satellite imagery (capturing different geographical locations, terrains and weather conditions) and lack of labeled data covering this diversity makes machine learning algorithms difficult to generalize for such tasks, especially across multiple domains. Re-training for new domain is both computationally and labor expansive mainly due to the cost of collecting pixel level labels required for the segmentation task. Domain adaptation algorithms have been proposed to enable algorithms trained on images of one domain (source) to work on images from other dataset (target). Unsupervised domain adaptation is a popular choice since it allows the trained model to adapt without requiring any ground-truth information of the target domain. On the other hand, due to the lack of strong spatial context and structure, in comparison to the ground imagery, application of existing unsupervised domain adaptation methods results in the sub-optimal adaptation. We thoroughly study limitations of existing domain adaptation methods and propose a weakly-supervised adaptation strategy where we assume image level labels are available for the target domain. More specifically, we design a built-up area segmentation network (as encoder-decoder), with image classification head added to guide the adaptation. The devised system is able to address the problem of visual differences in multiple satellite and aerial imagery datasets, ranging from high resolution (HR) to very high resolution (VHR), by investigating the latent space as well as the structured output space.

A realistic and challenging HR dataset is created by hand-tagging the 73.4 sq-km of Rwanda, capturing a variety of build-up structures over different terrain. The developed dataset is spatially rich compared to existing datasets and covers diverse built-up scenarios including built-up areas in forests and deserts, mud houses, tin and colored rooftops. Extensive experiments are performed by adapting from the single-source domain datasets, such as Massachusetts Buildings Dataset, to segment out the target domain. We achieve high gains ranging 11.6–52% in IoU over the existing state-of-the-art methods.

本文提出了一种新的领域自适应算法来应对卫星和航空图像提出的挑战，并证明了其在建立区域分割问题上的有效性。建筑面积估计是了解人类对环境的影响，公共政策的影响以及一般城市人口分析的重要组成部分。航空和卫星图像的多样性（捕获不同的地理位置，地形和天气条件）以及缺乏覆盖这种多样性的标记数据使得机器学习算法很难推广到此类任务，尤其是跨多个领域。对新域的重新训练既在计算上又在劳力扩展上，这主要是由于收集分割任务所需的像素级标签的成本所致。已经提出了域自适应算法，以使在一个域（源）的图像上训练的算法能够在来自其他数据集（目标）的图像上工作。无监督域自适应是一种流行的选择，因为它允许训练后的模型自适应而无需目标域的任何真实信息。另一方面，由于缺乏强大的空间背景和结构，与地面图像相比，应用现有的无监督域自适应方法会导致次优自适应。我们彻底研究了现有域自适应方法的局限性，并提出了一种弱监督的自适应策略，在该策略中，我们假定图像级别标签可用于目标域。更具体地说，我们设计了一个内置的区域分割网络（如编码器/解码器），带有图像分类头以指导适应。通过调查潜在空间和结构化输出空间，该设计的系统能够解决从高分辨率（HR）到超高分辨率（VHR）的多个卫星和航空图像数据集中视觉差异的问题。

通过手动标记73.4平方公里的卢旺达，创建一个现实而具有挑战性的HR数据集，捕获不同地形上的各种建筑结构。与现有数据集相比，已开发的数据集在空间上更为丰富，并涵盖了各种构建场景，包括森林和沙漠中的构建区域，泥屋，锡和有色屋顶。通过改编单源域数据集（例如马萨诸塞州建筑物数据集）以分割目标域，可以进行广泛的实验。与现有的最先进方法相比，我们在IoU中获得11.6-52％的高收益。