Feature-space oversampling for addressing class imbalance in SAR ship classification

Awais, Ch Muhammad, Reggiannini, Marco, Moroni, Davide and Karakuş, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 2025. Feature-space oversampling for addressing class imbalance in SAR ship classification. Presented at: IGARSS 2025, Brisbane, Australia, 03-08 August 2025. IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 2010-2014. 10.1109/igarss55030.2025.11242334

Full text not available from this repository.

Abstract

SAR ship classification faces the challenge of long-tailed datasets, which complicates the classification of underrepresented classes. Oversampling methods have proven effective in addressing class imbalance in optical data. In this paper, we evaluated the effect of oversampling in the feature space for SAR ship classification. We propose two novel algorithms inspired by the Major-to-minor (M2m) method M2mf, M2mu. The algorithms are tested on two public datasets, OpenSARShip (6 classes) and FuSARShip (9 classes), using three state-of-the-art models as feature extractors: ViT, VGG16, and ResNet50. Additionally, we also analyzed the impact of oversampling methods on different class sizes. The results demonstrated the effectiveness of our novel methods over the original M2m and baselines, with an average F1-score increase of 8.82% for FuSARShip and 4.44% for OpenSARShip.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	9798331508111
ISSN:	2153-6996
Last Modified:	09 Dec 2025 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/183036

Actions (repository staff only)

Edit Item