Feature-Attentioned Object Detection in Remote Sensing Imagery

In this work, we introduce a novel feature-attentioned object detection framework to boost its performance in remote sensing imagery, which can focus on learning these intrinsic representations from different aspects in an end-to-end framework. Firstly, when fusing multi-scale visual features of backbone network, we adopt the channel-wise and pixel-wise attentions to enhance these object-related representations and weaken the background/noise information. Secondly, an adaptive multiple receptive fields attention mechanism is employed to generate horizontal region proposals under the special situation where objects in the remote sensing imagery are always with different aspect ratios. Finally, the proposal-level feature attention is proposed to better consider both multi-layer convolutional and apparent representations so that the region of interest network can better predict the object-wise category and its corresponding location information. Comprehensive evaluations on DOTA and UCAS-AOD datasets well demonstrate the effectiveness of our feature-attentioned network for object detection in remote sensing imagery.