Automatic land use/land cover (LULC) classification from very high resolution (VHR) remote sensing images can provide us with rapid, large-scale, and fine-grained understanding of the urbanization and ecosystem processes on the Earth’s surface. Although the burgeoning use of deep learning technology has boosted land cover classification from VHR images, one of the key challenges has not been investigated in depth, i.e., the current data-driven deep learning models are heavily reliant on the high similarity between the distributions of the labeled training data, i.e., the source data, and the unlabeled data, i.e., the target data. However, in practice, this condition is rarely met, and preparing labels manually every time for the target data is unrealistic. In this paper, we comprehensively evaluate the domain adaptation methods for modern deep learning based classification models. Domain adaptation is aimed at narrowing the domain shift between the labeled source data and the unlabeled target data, providing a practical way for a deep learning based model to fully utilize historical training data and get rid of the need for continual manual work. Furthermore, we propose a novel two-stage Domain Adaptation method for Cross-Spatio-Temporal classification called the DACST method with the inputs of labeled source data and the unlabeled target data. It consists of an image-level adaptation stage that aligns the appearance of the source and target data and produces the target-stylized source images, and a feature-level adaptation stage that further narrows the domain shift in the deep feature space. DACST significantly improves the spatiotemporal transferability of the classification model, which is embedded in the feature-level adaptation stage, to output a satisfactory classification map. In this study, we also conducted a comprehensive performance evaluation of the conventional and deep learning based image-level and feature-level domain adaptation methods for VHR LULC classification. Both binary and multi-class classification was conducted in cross-temporal and cross-spatiotemporal scenes in five large-scale datasets from around the world. The very high performance and the best robustness of the proposed method suggests that a new baseline of cross-domain VHR land cover classification in the deep learning age is being witnessed. The experiments also indicate that both the conventional and deep learning based image-level domain adaptation methods function in various situations, but almost all the feature-level methods are highly unstable on different data.

来自超高分辨率 (VHR) 遥感图像的自动土地利用/土地覆盖 (LULC) 分类可以为我们提供对地球表面城市化和生态系统过程的快速、大规模和细粒度的了解。尽管深度学习技术的迅速使用促进了 VHR 图像的土地覆盖分类，但尚未深入研究关键挑战之一，即当前的数据驱动的深度学习模型严重依赖于分布之间的高度相似性。标记的训练数据，即源数据，和未标记的数据，即目标数据。然而，在实践中，这个条件很少满足，每次为目标数据手动准备标签是不现实的。在本文中，我们全面评估了基于现代深度学习的分类模型的领域适应方法。领域适应旨在缩小标记的源数据和未标记的目标数据之间的领域转移，为基于深度学习的模型充分利用历史训练数据并摆脱持续手动工作的需要提供了一种实用的方法。此外，我们提出了一种新的用于跨时空分类的两阶段域自适应方法，称为 DACST 方法，其输入为标记的源数据和未标记的目标数据。它由一个图像级适应阶段组成，该阶段对齐源数据和目标数据的外观并生成目标风格化的源图像，以及一个特征级适应阶段，该阶段进一步缩小深度特征空间中的域偏移。DACST 显着提高了分类模型的时空可迁移性，嵌入在特征级适应阶段，输出令人满意的分类图。在这项研究中，我们还对用于 VHR LULC 分类的传统和基于深度学习的图像级和特征级域适应方法进行了综合性能评估。在来自世界各地的五个大规模数据集中，在跨时空场景中进行了二分类和多分类。该方法的高性能和最佳鲁棒性表明，深度学习时代跨域 VHR 土地覆盖分类的新基线正在被见证。