Abstract:
Due to the high operational complexity of manual sample labeling for hyperspectral images (HSIs), few-shot learning (FSL) has been introduced to cope with the lack of tra...View moreMetadata
Abstract:
Due to the high operational complexity of manual sample labeling for hyperspectral images (HSIs), few-shot learning (FSL) has been introduced to cope with the lack of training samples in the field of HSI classification and achieved good results by virtue of its excellent performance. However, factors such as category labeling noise, category distinguishability in the metric space, and completeness of effective feature mining are still the primary considerations that significantly affect the stability and robustness of FSL. Therefore, this study proposes a framework of FSL with label smoothing and metric space optimization (LMFSL) for HSI classification. The framework first incorporates a category label smoothing strategy into FSL, which mitigates the effect of noise by constructing a novel category label smoothing module (CLSM), to reduce the confidence of the classifier. Meanwhile, the study also designs a metric space optimization module (MSOM), which prompts similar samples in the metric space to largely aggregate together by maximizing the intraclass similarity and minimizing the interclass similarity in a more flexible way, so as to improve the decision boundary of the model and enhance the recognition performance of the model. Furthermore, to achieve effective feature extraction in the context of a few labeled samples, a lightweight feature extractor, LFE-MAs, incorporating multiple attention mechanisms is designed to extract the HSI features with high efficiency and low computational cost. Experimental results on four public HSI datasets show that LMFSL outperforms other state-of-the-art methods in terms of classification accuracy with limited labeled samples and has lower computational complexity.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 62)