When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But, in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks do not consider the intra-domain gap in the target domain. In this article, to further minimize the complex intra-domain shift within the target domain in remote sensing, we propose a novel iterative intra-domain adaptation framework (IterDANet), which conducts inter-domain adaptation (InterDA), entropy-based ranking (ER), and iterative intra-domain adaptation (IntraDA). Specifically, first, to enhance the performance of InterDA built upon generative adversarial network (GAN)-based image-to-image (I2I) translation, we propose a new generator selection strategy (GSS) to assess and choose a well-trained generator for the inter-domain classifier. Then, to produce more accurate pseudolabels for IntraDA, we propose a new pseudolabel generation strategy (PLGS) to remove both high-entropy and low-confident pixels in predicted maps of inter-domain classifier. Finally, to better reduce the intra-domain gap, we propose to cluster all the target images into multiple subdomains using ER and iteratively align the cleanest subdomain with other noisy subdomains. The extensive experiments on the benchmark dataset, which includes cross-city aerial images, highlight the superiority and effectiveness of our IterDANet against the state-of-the-art UDA frameworks.

中文翻译：

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IterDANet：遥感图像语义分割的迭代域内适应


在对不断扩散的未标记遥感图像进行分割时，无监督域适应（UDA）已成为最关键的技术之一，并取得了显着的性能。但事实上，现有的UDA框架和监督学习方法之间仍然存在很大的性能差距，因为大多数UDA框架没有考虑目标域的域内差距。在本文中，为了进一步最小化遥感目标域内复杂的域内偏移，我们提出了一种新颖的迭代域内适应框架（IterDANet），该框架进行域间适应（InterDA）、基于熵的排序（ ER）和迭代域内适应（IntraDA）。具体来说，首先，为了增强基于生成对抗网络（GAN）的图像到图像（I2I）转换的 InterDA 的性能，我们提出了一种新的生成器选择策略（GSS）来评估和选择训练有素的生成器域间分类器。然后，为了为 IntraDA 生成更准确的伪标签，我们提出了一种新的伪标签生成策略（PLGS），以删除域间分类器的预测图中的高熵和低置信度像素。最后，为了更好地减少域内差距，我们建议使用 ER 将所有目标图像聚类到多个子域中，并迭代地将最干净的子域与其他噪声子域对齐。对基准数据集（包括跨城市航空图像）进行的广泛实验凸显了我们的 IterDANet 相对于最先进的 UDA 框架的优越性和有效性。