Few-Shot Object Detection on Remote Sensing Images

Xiang Li, Jingyu Deng, Yi Fang

Research output: Contribution to journalArticlepeer-review


In this article, we deal with the problem of object detection on remote sensing images. Previous researchers have developed numerous deep convolutional neural network (CNN)-based methods for object detection on remote sensing images, and they have reported remarkable achievements in detection performance and efficiency. However, current CNN-based methods often require a large number of annotated samples to train deep neural networks and tend to have limited generalization abilities for unseen object categories. In this article, we introduce a metalearning-based method for few-shot object detection on remote sensing images where only a few annotated samples are needed for the unseen object categories. More specifically, our model contains three main components: a metafeature extractor that learns to extract metafeature maps from input images, a feature reweighting module that learns class-specific reweighting vectors from the support images and use them to recalibrate the metafeature maps, and a bounding box prediction module that carries out object detection on the reweighted feature maps. We build our few-shot object detection model upon the YOLOv3 architecture and develop a multiscale object detection framework. Experiments on two benchmark data sets demonstrate that with only a few annotated samples, our model can still achieve a satisfying detection performance on remote sensing images, and the performance of our model is significantly better than the well-established baseline models.

Original languageEnglish (US)
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - 2022


  • Few-shot learning
  • You-Only-Look-Once (YOLO)
  • metalearning
  • object detection
  • remote sensing images

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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