In this paper, we introduce a new building dataset and propose a novel domain generalization method to facilitate the development of building extraction from high-resolution remote sensing images. The problem with the current building datasets involves that they lack diversity, the quality of the labels is unsatisfactory, and they are hardly used to train a building extraction model with good generalization ability, so as to properly evaluate the real performance of a model in practical scenes. To address these issues, we built a diverse, large-scale, and high-quality building dataset named the WHU-Mix building dataset, which is more practice-oriented. The WHU-Mix building dataset consists of a training/validation set containing 43,727 diverse images collected from all over the world, and a test set containing 8402 images from five other cities on five continents. In addition, to further improve the generalization ability of a building extraction model, we propose a domain generalization method named batch style mixing (BSM), which can be embedded as an efficient plug-and-play module in the frond-end of a building extraction model, providing the model with a progressively larger data distribution to learn data-invariant knowledge. The experiments conducted in this study confirmed the potential of the WHU-Mix building dataset to improve the performance of a building extraction model, resulting in a 6-36% improvement in mIoU, compared to the other existing datasets. The adverse impact of the inaccurate labels in the other datasets can cause about 20% IoU decrease. The experiments also confirmed the high performance of the proposed BSM module in enhancing the generalization ability and robustness of a model, exceeding the baseline model without domain generalization by 13% and the recent domain generalization methods by 4-15% in mIoU.
翻译:在本文中,我们引入了一个新的建筑数据集,并提出了一个新的领域概括化方法,以促进从高分辨率遥感图像中提取建筑数据。当前建筑数据集的问题在于它们缺乏多样性,标签的质量不令人满意,它们很难用来培训具有良好概括化能力的建筑提取模型,以便适当评估一个模型在实际场景中的真实性能。为了解决这些问题,我们建立了一个多样化、大规模和高质量的建筑数据集,名为WHU-Mix 数据库,它更注重实践。WHU-Mix 构建数据集包括一个培训/校验数据集,包含从世界各地收集的43,727多种图像,以及包含5个大洲其他5个城市8402个图像的测试集。此外,为了进一步提高建筑提取模型在实际场中的总体性能,我们提出了名为批量制混合(BSM)的域化方法,该方法可以嵌入一个高效的插入和播放模块。WHU-M36 建模模型的精确性能性能模型包括一个不断增强的模型,该模型在不断增强的精确性能性能分析中,通过不断提高的数据模型,通过不断提高的模型进行其他数据流化数据流化数据流化,从而提高现有数据流化数据流化的一般性数据流化数据流化数据,从而提高现有数据流化数据流化数据流化数据流化中进行其他数据流化的模型。