2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-9.5
Paper Title ADAPTING INTRA-CLASS VARIATIONS FOR SAR IMAGE CLASSIFICATION
Authors Tsenjung Tai, Masato Toda, NEC Corporation, Japan
SessionARS-9: Interpretation, Understanding, Retrieval
LocationArea I
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a semi-supervised domain adaptation (SSDA) method for Synthetic Aperture Radar (SAR) image classification. SAR imagery is important in ground activity monitoring, but its wide application is impeded due to a lack of annotations. SSDA methods transfer class-discriminative knowledge from a fully-labeled source dataset to a scarcely-labeled target dataset. However, conventional methods often train models which overfit to labeled target data and fail on unlabeled data. To overcome this, we propose to additionally adapt intra-class variations. Specifically, a conversion network is trained to learn from source data the image feature variations caused by the change of image capturing angle. Then synthetic data, which represent a generalized target domain distribution, are estimated by applying the conversion to labeled target data. Our method improves the accuracy of the state-of-the-art SSDA approach from 64.28% to 80.40% in three-shot cases on the SAR ground vehicle dataset.