Analyzing the geographical information for the Plateau Lake region with remote sensing images (RSI) is an emerging technology to monitor the changes of the ecological environment. To alleviate the requirement of abundant labels for supervised RSI segmentation, the Cross-consistency Guiding Semi-supervised Learning (SSL) Semantic Segmentation Network is proposed, and it can perform high-quality multi-category semantic segmentation for complex remote sensing scenes with limited quantity of labeled images. Firstly, based on the SSL semantic segmentation framework, through the cross-consistency method training a teacher model with less annotated images and plentiful unannotated images, then generating higher-quality pseudo labels to guide the learning process of the student model. Secondly, dense conditional random field and mask hole repair are used to patch and fill the flaw areas of pseudo-labels based on the pixel features of position, color, and texture, further improving the granularity and reliability of the student model training dataset. Additionally, to improve the accuracy of the model, we designed a strong data augmentation (SDA) method based on a stochastic cascaded strategy, which connects multiple augmentation techniques in random order and probability cascade to generate new training samples. It mimics a variety of image transformations and noise conditions that occur in the real world to enhance the robustness in complex scenarios. To validate the effectiveness of CGSNet in complex remote sensing scenes, extended experiments are conducted on the self-built plateau lake RSI dataset and two public multi-category RSI datasets. The experiment results demonstrate that, compared with other state-of-the-art SSL methods, the proposed CGSNet achieves the highest 77.47% mIoU and 87.06% F1 scores with a limited quantity of annotated data.

中文翻译：

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CGSNet：高原湖泊遥感交叉一致性引导半监督语义分割网络


利用遥感影像（RSI）分析高原湖区地理信息是监测生态环境变化的一项新兴技术。为了缓解监督RSI分割对丰富标签的需求，提出了交叉一致性引导半监督学习（SSL）语义分割网络，可以对数量有限的复杂遥感场景进行高质量的多类别语义分割的标记图像。首先，基于SSL语义分割框架，通过交叉一致性方法用较少标注图像和大量未标注图像训练教师模型，然后生成更高质量的伪标签来指导学生模型的学习过程。其次，根据位置、颜色、纹理等像素特征，采用密集条件随机场和掩模孔修复技术对伪标签的缺陷区域进行修补和填充，进一步提高学生模型训练数据集的粒度和可靠性。此外，为了提高模型的准确性，我们设计了一种基于随机级联策略的强数据增强（SDA）方法，该方法以随机顺序和概率级联连接多种增强技术来生成新的训练样本。它模仿现实世界中发生的各种图像变换和噪声条件，以增强复杂场景下的鲁棒性。为了验证CGSNet在复杂遥感场景中的有效性，在自建的高原湖泊RSI数据集和两个公共多类别RSI数据集上进行了扩展实验。实验结果表明，与其他最先进的 SSL 方法相比，所提出的 CGSNet 达到了最高的 77。在注释数据数量有限的情况下，mIoU 为 47%，F1 分数为 87.06%。