A Contextual Bidirectional Enhancement Method for Remote Sensing Image Object Detection

Jun Zhang, Changming Xie, Xia Xu, Zhenwei Shi, Bin Pan

Abstract: In remote sensing images, the backgrounds of objects include crucial contextual information that may contribute to distinguishing objects. However, there are at least two issues that should be addressed: not all the backgrounds are beneficial, and object information may be suppressed by backgrounds. To address these problems, in this article, we propose the contextual bidirectional enhancement (CBD-E) method to simultaneously remove unexpected background information and enhance objects' features. CBD-E integrates the features of different background regions sequentially in two directions. On the one hand, a gate function is used to filter out unexpected information in the background and thus improve the recall of detection. On the other hand, a spatial-group-based visual attention mechanism is adopted to enhance the features of objects to reduce the false alarm. The gate function provides an approach to selecting meaningful information in the background, while the spatial-group- based visual attention mechanism enhances the information control ability of the gate function. In the experiments, we have validated the effectiveness of both the gate function and the visual attention mechanism and further demonstrated that the proposed contextual fusion strategy performs well on two published data sets.