Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Zhang, P.; Li, Y.; Wang, D.; Bai, Y. Few-shot Classification of Aerial Scene Images via Meta-learning. Preprints 2020, 2020100033 (doi: 10.20944/preprints202010.0033.v1). Zhang, P.; Li, Y.; Wang, D.; Bai, Y. Few-shot Classification of Aerial Scene Images via Meta-learning. Preprints 2020, 2020100033 (doi: 10.20944/preprints202010.0033.v1).

Abstract

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 outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale and 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

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