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 language | English (US) |
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Title of host publication | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1074-1077 |
Number of pages | 4 |
ISBN (Electronic) | 9781728163741 |
DOIs | |
State | Published - Sep 26 2020 |
Externally published | Yes |
Event | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
Conference | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 |
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Country/Territory | United States |
City | Virtual, Waikoloa |
Period | 9/26/20 → 10/2/20 |
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