The rapid advancement in high-resolution satellite remote sensing data acquisition, particularly those achieving submeter precision, has uncovered the potential for detailed extraction of surface architectural features. However, the diversity and complexity of surface distributions frequently lead to current methods focusing exclusively on localized information of surface features. This often results in significant intraclass variability in boundary recognition and between buildings. Therefore, the task of fine-grained extraction of surface features from high-resolution satellite imagery has emerged as a critical challenge in remote sensing image processing. In this work, we propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features, thereby enabling the refined extraction of landmark buildings from high-resolution satellite remote sensing imagery. The Pyramid Vision Transformer captures these global features, which are subsequently refined by the Feature Aggregation Module and merged into a cohesive representation by the Difference Elimination Module. In addition, to ensure a comprehensive feature map, we have incorporated the Receptive Field Block and Dual Attention Module, expanding the receptive field and intensifying attention across spatial and channel dimensions. Extensive experiments on multiple datasets have validated the outstanding capability of FANet in extracting features from high-resolution satellite images. This signifies a major breakthrough in the field of remote sensing image processing. We will release our code soon.

翻译：高分辨率卫星遥感数据获取技术的飞速进步，特别是达到亚米级精度的技术，揭示了地表建筑特征精细提取的潜力。然而，地表分布的多样性与复杂性常导致现有方法仅聚焦于地表特征的局部信息，致使边界识别及建筑间类内差异显著。因此，从高分辨率卫星影像中实现地表特征的精细提取已成为遥感图像处理领域的关键挑战。本文提出特征聚合网络（FANet），专注于提取全局与局部特征，从而实现对高分辨率卫星遥感影像中标志性建筑的精细化提取。金字塔视觉变换器捕获全局特征，随后通过特征聚合模块进行细化，并由差异消除模块融合为统一表征。此外，为确保特征图的完整性，我们引入了感受野模块与双注意力模块，以扩展感受野并强化空间与通道维度的注意力。在多个数据集上的广泛实验验证了FANet在高分辨率卫星图像特征提取中的卓越能力，这标志着遥感图像处理领域的重大突破。我们将于近期公开代码。