High-quality land-use/land-cover mapping with optical remote sensing images yet presents significant work. Even though fully convolutional semantic segmentation models have recently contributed to popular solutions, the lack of annotation data may lead to severe degradations in their inference performance. Besides, the category confusion in high-resolution representations will further exacerbate the adverse effects. In this paper, we propose a category-sensitive semi-supervised semantic segmentation framework to address these weaknesses by employing massive unlabeled data. With the perturbations from adopted hybrid data augmentation structures, we first focus on the output space and execute regularization constraints to learn category-specific discriminative features. It is formulated with a consistency self-training procedure where a dynamic class-balanced threshold selection scheme is proposed to provide high-confident pseudo supervisions for each category. In addition, we introduce pixel-wise contrastive learning on the common embedding space from both labeled and unlabeled data domains to further facilitate the semantic dependencies among category features, in which the reliable labels are leveraged as guidance for pixel sample selection. We verify the proposed framework on two benchmark land-use/land-cover datasets, and the experimental results demonstrate its competitive performance to other state-of-the-art semi-supervised methods.

中文翻译：

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用于光学遥感图像土地利用/土地覆盖制图的类别敏感半监督语义分割框架


利用光学遥感图像绘制高质量的土地利用/土地覆盖图仍然是一项重要的工作。尽管全卷积语义分割模型最近为流行的解决方案做出了贡献，但注释数据的缺乏可能会导致其推理性能严重下降。此外，高分辨率表示中的类别混乱也会进一步加剧不利影响。在本文中，我们提出了一种类别敏感的半监督语义分割框架，通过使用大量未标记数据来解决这些弱点。由于采用的混合数据增强结构的扰动，我们首先关注输出空间并执行正则化约束来学习特定于类别的判别特征。它是用一致性自训练程序制定的，其中提出了动态类平衡阈值选择方案，为每个类别提供高可信度的伪监督。此外，我们在来自标记和未标记数据域的公共嵌入空间上引入逐像素对比学习，以进一步促进类别特征之间的语义依赖性，其中利用可靠标签作为像素样本选择的指导。我们在两个基准土地利用/土地覆盖数据集上验证了所提出的框架，实验结果证明了其与其他最先进的半监督方法的竞争性能。