Deep learning robustness to domain shifts during seasonal variations

Voulgaris, Georgios, Philippides, Andrew and Quadrianto, Novi (2022) Deep learning robustness to domain shifts during seasonal variations. International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia, 17th - 22nd July 2022. Published in: IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) proceedings. 1-4. IEEE ISSN 2153-7003 ISBN 9781665427920

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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.

Item Type: Conference Proceedings
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Deep Learning, Computer Vision, Remote Sensing, Feature Bias
Schools and Departments: School of Engineering and Informatics > Informatics
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Date Deposited: 27 May 2022 07:40
Last Modified: 27 Oct 2022 09:38

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