  Oriented object detection in remote sensing images has made great progress in
recent years. However, most of the current methods only focus on detecting
targets, and cannot distinguish fine-grained objects well in complex scenes. In
this technical report, we analyzed the key issues of fine-grained object
recognition, and use an oriented feature alignment network (OFA-Net) to achieve
high-performance fine-grained oriented object recognition in optical remote
sensing images. OFA-Net achieves accurate object localization through a rotated
bounding boxes refinement module. On this basis, the boundary-constrained
rotation feature alignment module is applied to achieve local feature
extraction, which is beneficial to fine-grained object classification. The
single model of our method achieved mAP of 46.51\% in the GaoFen competition
and won 3rd place in the ISPRS benchmark with the mAP of 43.73\%.
