The feature representation capability of the object detection model is considerably reduced if the available training samples are few-shot. The challenges of arbitrary orientation and complex background of ground objects are universal in very high spatial resolution (VHR) remote sensing imageries, resulting in massive difficulty on few-shot object detection task. However, existing methods for few-shot object detection are not explored in terms of the capabilities of feature representation in remote sensing images. To solve these issues, we propose a few-shot object detection method incorporating multiscale object contrastive learning. First, our method performs contrastive learning to represent the object feature fully by adopting the Siamese network structure in the few-shot training. On the one hand, the Siamese structure’s lower branch is embedded in the contrastive learning process to cope with the challenge of the complexity of images; on the other hand, a multiscale instance feature module is designed to obtain multiscale contrastive information. Second, we leverage the proposed contrastive multiscale proposal (CMSP) to make full use of multiscale information. It can promote our method to fit the data better. The experimental results show that the proposed method has good feature representation capabilities in the few-shot object detection. Moreover, the performance of the proposed method is better than that of related methods. The source code is available at https://github.com/RS-CSU/MSOCL.