SCL-MLNet: Boosting Few-Shot Remote Sensing Scene Classification via Self-Supervised Contrastive Learning

Few-shot classification aims at recognizing novel categories from low data regimes based on prior knowledge. However, the existing methods for few-shot scene classification have limitations on using few annotated data and do not fully consider the intra-class samples with classification targets in d...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-12
Main Authors: Li, Xiaomin, Shi, Daqian, Diao, Xiaolei, Xu, Hao
Format: Article
Language:English
Subjects:
Adaptation models
Algorithms
Classification
Feature extraction
Feature representation
few-shot learning
Fuses
Image classification
Learning
Machine learning
Measurement
Remote sensing
remote sensing (RS) scene classification
self-supervised contrastive learning (SCL)
Task analysis
Training
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Summary:Few-shot classification aims at recognizing novel categories from low data regimes based on prior knowledge. However, the existing methods for few-shot scene classification have limitations on using few annotated data and do not fully consider the intra-class samples with classification targets in different sizes, which lead to poor feature representation. To address these problems, this study introduces an end-to-end framework called self-supervised contrastive learning-based metric learning network (SCL-MLNet) for few-shot remote sensing (RS) scene classification. On one hand, we weave self-supervised contrastive learning into few-shot classification algorithms through multi-task learning, enabling feature extractors to learn representative image features from few annotated samples. Moreover, we devise a new loss function to train the proposed model end-to-end and speed up the convergence of the model. On the other hand, considering the differences between intra-class samples, we introduce a novel attention module embedded in the feature extractor to fuse multi-scale spatial features from the classification targets in different sizes. In our experiments, SCL-MLNet is evaluated on three public benchmark datasets. The results demonstrate that SCL-MLNet achieves state-of-the-art performance for few-shot remote sensing scene classification.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3109268