Few-Shot Object Detection in Remote Sensing Images With Multiscale Spatial Selective Attention

Few-shot object detection (FSOD) leverages limited labeled data and substantial unlabeled data for detection. However, these approaches mainly target natural images and ignore the spatial relationships and contextual information between objects in remote sensing images (RSIs). To overcome these challenges, this letter introduces a novel method for detecting few-shot objects in RSI. First, we propose a new attention, called multiscale spatial selective attention (MSSSA). This attention spatially selects feature maps from convolution kernels of different scales through spatial selection, focusing the network on the most relevant region of spatial context. Then, our proposed pixel-level feature extractor module (PLFEM) was used in the first stage of FSOD, providing pixel-level object position information to reduce false and missed detection. To evaluate the proposed method, we carry out comprehensive experiments on the DIOR dataset. The results show that the novel class mAP of our method reaches 38.2% in ten shots, an increase of 3.0% compared with the baseline, significantly improving the accuracy of FSOD in RSI.