Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level context information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.

翻译：语义分割是高分辨率遥感图像自动解译中的关键技术，近年来在遥感领域受到广泛关注。深度卷积神经网络凭借其层次化表征能力，已成功应用于高分辨率遥感图像的语义分割任务。然而，该类方法对大规模密集标注训练数据的严重依赖以及对数据分布变化的敏感性，极大限制了其在遥感图像语义分割中的实际应用潜力。为此，本文提出一种新型无监督域适应语义分割网络——MemoryAdaptNet。该网络构建了输出空间对抗学习机制，以弥合源域与目标域之间的域分布差异，并削弱域偏移的影响。具体而言，我们嵌入一个不变特征记忆模块来存储域级不变上下文信息，这是因为对抗学习获取的特征仅能表征当前有限输入的变异特征。该模块通过类别注意力驱动的域级不变上下文聚合模块与当前的伪不变特征进行集成，以进一步增强像素表征能力。此外，采用基于熵的伪标签过滤策略，利用当前目标图像中高置信度的伪不变特征更新记忆模块。在三个跨域任务上的大量实验表明，本文提出的MemoryAdaptNet显著优于现有最优方法。