Working Paper Article Version 2 This version is not peer-reviewed

Few-shot Classification of Aerial Scene Images via Meta-learning

Version 1 : Received: 1 October 2020 / Approved: 2 October 2020 / Online: 2 October 2020 (09:24:19 CEST)
Version 2 : Received: 14 December 2020 / Approved: 15 December 2020 / Online: 15 December 2020 (13:21:49 CET)

A peer-reviewed article of this Preprint also exists.

Journal reference: Remote Sensing 2021
DOI: 10.3390/rs13010108


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.

Subject Areas

aerial scene classification; remote-sensing image classification; few-shot learning; meta-learning

Comments (1)

Comment 1
Received: 15 December 2020
Commenter: Ying Li
Commenter's Conflict of Interests: Author
Comment: We re-implement three methods (i.e., MAML, ProtoNet, RelationNet) with ResNet-12 backbone for a fair comparison. Besides that, we have added three recent methods for comparison, including TADAM (NIPS2018), MetaOpt(CVPR2019), DSN-MR(CVPR2020). Finally, we added an ablation study of the
effect of metrics.
This version was resubmitted to remote sensing on 2020-11-16.
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