preprints.org > mathematics & computer science > other > 202010.0033.v2

Working Paper Article Version 2 This version is not peer-reviewed

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)

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.

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