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.

翻译：卫星影像对于环境监测和城市规划等任务至关重要，通常依赖语义分割或土地利用土地覆盖（LULC）分类来对每个像素进行归类。尽管深度神经网络（DNNs）取得了显著进展，但其在分割任务中的性能仍受限于标注数据稀缺、类别不平衡以及卫星影像固有的变异性和复杂性等挑战。为缓解这些问题，本研究探讨了剪切粘贴（Cut-and-Paste）增强技术在卫星影像语义分割中的有效性。我们将这一通常需要标注实例的数据增强方法适配至语义分割场景，通过利用语义分割标签中的连通组件提取实例，并在训练过程中随机粘贴这些实例。基于DynamicEarthNet数据集和U-Net模型的评估表明，该增强技术显著将测试集的平均交并比（mIoU）分数从37.9提升至44.1。这一发现凸显了剪切粘贴增强技术在提升卫星影像语义分割模型泛化能力方面的潜力。