  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.
