Satellite imagery is crucial for tasks like environmental monitoring and
urban planning. Typically, it relies on semantic segmentation or Land Use Land
Cover (LULC) classification to categorize each pixel. Despite the advancements
brought about by Deep Neural Networks (DNNs), their performance in segmentation
tasks is hindered by challenges such as limited availability of labeled data,
class imbalance and the inherent variability and complexity of satellite
images. In order to mitigate those issues, our study explores the effectiveness
of a Cut-and-Paste augmentation technique for semantic segmentation in
satellite images. We adapt this augmentation, which usually requires labeled
instances, to the case of semantic segmentation. By leveraging the connected
components in the semantic segmentation labels, we extract instances that are
then randomly pasted during training. Using the DynamicEarthNet dataset and a
U-Net model for evaluation, we found that this augmentation significantly
enhances the mIoU score on the test set from 37.9 to 44.1. This finding
highlights the potential of the Cut-and-Paste augmentation to improve the
generalization capabilities of semantic segmentation models in satellite
imagery.

卫星影像重要用于环境监测和城市规划，本研究探索了一个适用于语义分割的剪切粘贴增强技术，通过利用语义分割标签中的联通组件，从而在训练过程中随机粘贴提取的实例，实验结果表明，这种增强技术显著提升了语义分割模型的平均交并比得分，从 37.9 提升至 44.1，彰显了其在卫星影像中提高模型泛化能力的潜力。