Hyperspectral Image Classification Based on Multi-stage Vision Transformer with Stacked Samples

Xiaoyue Chen, Sei Ichiro Kamata, Weilian Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hyperspectral image classification (HSIC) is a task assigning the correct label to each pixel. It is a hot topic in the remote sensing field, which has been processed in several deep learning methods. Recently, there are some works that apply Vision Transformer (ViT) methods to the HSIC task, but the performance is not as good as some CNN-structured methods, considering that Vision Transformer uses attention to capture global information but ignores local characteristics. In this paper, a multi-stage Vision Transformer model referring to the feature extraction structure of CNN is proposed, and the result shows the realizability and reliability. Besides, experiments show that the modified ViT structure needs more samples for training. An innovative data augmentation method is used to generate extended samples with virtual yet reliable labels. The generated samples are combined with the original ones as the stacked samples, which are used for the following feature extraction process. Experiments explain the optimization of the multi-stage Vision Transformer structure with stacked samples in the accuracy term compared with other methods.

Original languageEnglish
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages441-446
Number of pages6
ISBN (Electronic)9781665495325
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: 2021 Dec 72021 Dec 10

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2021-December
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand
CityAuckland
Period21/12/721/12/10

Keywords

  • Hyperspectral image classification
  • Vision Transformer
  • data augmentation
  • deep learning
  • image processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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