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Class Shared Dictionary Learning for Few-Shot Remote Sensing Scene Classification
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DOI：10.1109/LGRS.2022.3180791.png)
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Abstract
En 中文
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.
Keywords:
Feature extraction
Task analysis
Remote sensing
Image analysis
Dictionaries
Training
Data mining
Dictionary learning
few-shot learning (FSL)
remote sensing scene classification (RSSC)
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