Domain adaptive segmentation has recently gained more and more attention in the remote sensing field. However, current methods often generate a significant number of uncertain examples, i.e., noisy pseudo-labels, in the target domain, which adversely affects model convergence. To solve this issue, an uncertain example mining network is proposed for domain adaptive segmentation of remote sensing images. Specifically, a novel strategy called multilevel pseudo-label correcting (MPC) is proposed to correct the pseudo-labels in class, pixel, and superpixel levels. In this way, more reliable pseudo-labels can be selected for the subsequent training stage. Furthermore, a noise-robust example mining strategy, termed uncertainty-based valuable example mining (UVEM), is proposed to prioritize confident examples with significant gradients for training effectively. Extensive empirical evaluations on IsprsDA and LoveDA datasets demonstrate that the proposed method outperforms previous approaches, establishing state-of-the-art results in domain adaptive remote sensing image segmentation (RSIS). The code will be available at https://github.com/StuLiu/UemDA .

中文翻译：

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遥感图像域自适应分割的不确定挖掘网络示例


域自适应分割最近在遥感领域受到越来越多的关注。然而，当前的方法经常在目标域中生成大量不确定的示例，即噪声伪标签，这对模型收敛产生不利影响。为了解决这个问题，提出了一种用于遥感图像域自适应分割的不确定实例挖掘网络。具体来说，提出了一种称为多级伪标签校正（MPC）的新策略来校正类、像素和超像素级别的伪标签。这样就可以为后续的训练阶段选择更可靠的伪标签。此外，提出了一种抗噪声示例挖掘策略，称为基于不确定性的有价值示例挖掘（UVEM），以优先考虑具有显着梯度的置信示例，以便有效地进行训练。对 IsprsDA 和 LoveDA 数据集的广泛实证评估表明，所提出的方法优于以前的方法，在域自适应遥感图像分割（RSIS）中建立了最先进的结果。该代码可在 https://github.com/StuLiu/UemDA 获取。