Few-shot classification of aerial scene images via meta-learning

Pei Zhang, Yunpeng Bai, Dong Wang, Bendu Bai, Ying Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (SciVal)
50 Downloads (Pure)


Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

Original languageEnglish
Article number108
Number of pages21
JournalRemote Sensing
Issue number1
Publication statusPublished - 31 Dec 2020
Externally publishedYes


  • Aerial scene classification
  • Few-shot learning
  • Meta-learning
  • Remote-sensing image classification


Dive into the research topics of 'Few-shot classification of aerial scene images via meta-learning'. Together they form a unique fingerprint.

Cite this