featurebiases.pdf (11.35 MB)
Deep learning robustness to domain shifts during seasonal variations
conference contributionposted on 2023-06-10, 02:37 authored by Georgios Voulgaris, Andy PhilippidesAndy Philippides, Novi QuadriantoNovi Quadrianto
In certain geographic locations like South Asia, the landscape changes dramatically between dry and wet seasons. The main factor responsible for this variation is the flora that transforms the landscape between seasons. These transformations can affect the performance of deep learning models trained to analyse satellite images, especially if there are domain shifts between training and testing data distributions. The current work shows that an architecture which employs a Gabor convolutional layer as the first layer of a deep network input focuses on more salient parts of the image than one which uses a standard convolutional layer meaning that removing colour information is less damaging than for the standard network. Further we show that the proposed architecture is robust in the presence of domain shifts due to seasonal data variations.
- Accepted version
JournalIEEE International Symposium on Geoscience and Remote Sensing (IGARSS) proceedings
Event nameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Event locationKuala Lumpur, Malaysia
Event date17th - 22nd July 2022
Department affiliated with
- Informatics Publications
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