Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series

Benjamin Lucas, Charlotte Pelletier, Daniel Schmidt, Geoffrey I. Webb, Francois Petitjean

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

12 Scopus citations

Abstract

Land cover maps are vitally important to many elements of environmental management. However the machine learning algorithms used to produce them require a substantive quantity of labelled training data to reach the best levels of accuracy. When researchers wish to map an area where no labelled training data are available, one potential solution is to use a classifier trained on another geographical area and adapting it to the target location-this is known as Unsupervised Domain Adaptation (DA). In this paper we undertake the first experiments using unsupervised DA methods for the classification of satellite image time series (SITS) data. Our experiments draw the interesting conclusion that existing methods provide no benefit when used on SITS data, and that this is likely due to the temporal nature of the data and the change in class distributions between the regions. This suggests that an unsupervised domain adaptation technique for SITS would be extremely beneficial for land cover mapping.

Original languageEnglish (US)
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1074-1077
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - Sep 26 2020
Externally publishedYes
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: Sep 26 2020Oct 2 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period9/26/2010/2/20

Keywords

  • Deep Learning
  • Domain Adaptation
  • Land Cover Map
  • Satellite Image Time Series
  • Transfer Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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