全局关系注意力引导场景约束的高分辨率遥感影像目标检测

高分辨率遥感影像中地物目标往往与所处场景类别息息相关，如能充分利用场景对地物目标的约束信息，有望进一步提升目标检测性能。考虑到场景信息和地物目标之间的关联关系，提出全局关系注意力(RGA)引导场景约束的高分辨率遥感影像目标检测方法。首先在多尺度特征融合检测器的基础网络之后，加入全局关系注意力学习全局场景特征；然后以学到的全局场景特征作为约束，结合方向响应卷积模块和多尺度特征模块进行目标预测；最后利用两个损失函数联合优化网络实现目标检测。在NWPU VHR-10数据集上进行了4组实验，在场景信息约束的条件下取得了更好的目标检测性能。

Scene Constrained Object Detection Method in High-Resolution Remote Sensing Images by Relation-Aware Global Attention

Ground objects in high-resolution remote sensing images are often closely related to the scene categories. If the constraint information of the scene on the ground object can be usefully employed, it is expected to improve further the performance of object detection. Considering the relationship between scene information and objects, a scene constrained object detection method in high-resolution remote sensing images by Relation-aware Global Attention (RGA) is proposed. First, the global scene features are learned by adding the global relational attention to the basic network in Feature fusion and Scaling-based Single Shot Detector (FS-SSD). Then, object is predicted by combining the oriented response convolution module with the multiscale feature module under the constraints of learned global scene features. Finally, two loss functions are used to optimize jointly the network to achieve object detection. Four experiments are conducted on NWPU VHR-10 dataset and better object detection performance is achieved under the constraints of scene information.