Unsupervised Domain Adaptation Techniques for Classification of Satellite Image Time Series
- Resource Type
- Conference
- Authors
- Lucas, Benjamin; Pelletier, Charlotte; Schmidt, Daniel; Webb, Geoffrey I.; Petitjean, Francois
- Source
- IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2020 - 2020 IEEE International. :1074-1077 Sep, 2020
- Subject
- Aerospace
Computing and Processing
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Time series analysis
Transforms
Training
Satellites
Support vector machines
Radio frequency
Kernel
Domain Adaptation
Transfer Learning
Satellite Image Time Series
Land Cover Map
Deep Learning
- Language
- ISSN
- 2153-7003
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.