Aiming at the problem of confusion between foreground class and background class caused by the lack of example labeling in few-shot remote sensing images, a object detection method for few-shot remote sensing images combined with pixel attention and decoupled classifier is proposed. In this method, a novel pixel attention feature pyramid structure is designed to capture more important spatial and channel semantic information, so as to highlight the key features of the objects while suppressing background noise. In addition, the standard classifier is decoupled into two parallel detection heads to process the foreground classes and the noisy background classes respectively to alleviate the bias classification problem of the classifier. The proposed method is experimentally carried out on two public remote sensing datasets, and the results show that compared with the current new methods, the average accuracy of the proposed method is improved by 4%-7% on the DIOR dataset and 11%-17% on the NWPU VHR-10 dataset, and the detection performance is good.