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Class Shared Dictionary Learning for Few-Shot Remote Sensing Scene Classification


Abstract:

In the field of remote sensing, it is infeasible to collect a large number of labeled samples due to imaging equipment and imaging environment. Few-shot learning (FSL) is...Show More

Abstract:

In the field of remote sensing, it is infeasible to collect a large number of labeled samples due to imaging equipment and imaging environment. Few-shot learning (FSL) is the dominant method to alleviate this problem, which pursues quickly adapting to novel categories from a limited number of labeled samples. The few-shot remote sensing scene classification (RSSC) generally includes the pretraining and meta-test phases. However, a “negative transfer” problem exists that data categories in both the phases are different. It causes the pretrained feature extractor to be unable well-adapted to the novel data category. This letter proposes class shared dictionary learning (CSDL) for few-shot RSSC to address this issue. Specifically, this letter designs the mirror-based feature extractor (MFE) in the pretraining phase, constructing a self-supervised classification task to improve the feature extractor robustness. Furthermore, this letter proposes a class shared dictionary (CSD) classifier based on dictionary learning. The CSD projects the novel data feature in meta-test into subspace to reconstruct more discriminative features and complete the classification task. Extensive experiments on remote sensing datasets have demonstrated that the proposed CSDL achieves advanced classification performance.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 19)
Article Sequence Number: 6512805
Date of Publication: 08 June 2022

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