A Meta-Learning Framework for Few-Shot Classification of Remote Sensing Scene

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

6 Citations (SciVal)

Abstract

While achieving remarkable success in remote sensing (RS) scene classification for the past few years, convolutional neural network (CNN) based methods suffer from the demand for large amounts of training data. The bottleneck in prediction accuracy has shifted from data processing limits toward a lack of ground truth samples, usually collected manually by experienced experts. In this work, we provide a meta-learning framework for few-shot classification of RS scene. Under the umbrella of meta-learning, we show it is possible to learn much information about a new category from only 1 or 5 samples. The proposed method is based on Prototypical Networks with a pre-trained stage and a learnable similarity metric. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN, on two challenging datasets: NWPU-RESISC45 and RSD46-WHU.

Original languageEnglish
Title of host publicationICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages4590-4594
Number of pages5
Volume2021-June
DOIs
Publication statusPublished - 06 Jun 2021

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Keywords

  • Few-shot learning
  • Meta-learning
  • Remote sensing
  • Scene classification

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