Few-Shot Hyperspectral Image Classification With Deep Fuzzy Metric Learning

Abstract

Deep metric learning (DML) has shown promising results in few-shot hyperspectral image (HSI) classification. The core idea of DML is to learn a generalized metric space, in which pixels from unseen classes can be effectively classified with only a few labeled samples. However, the existing DML methods mainly adopt traditional Euclidean distance to achieve the feature metric, which ignores the category uncertainty of spatial-spectral features in mixed and edge pixels. To address this issue, we fully exploit fuzzy logic theory and propose a deep fuzzy metric learning (DFML) method for few-shot HSI classification. First, we design a novel hybrid CNN-transformer spatial-spectral feature extraction network to fully capture the spatial-spectral features of HSI pixels. Then, a fuzzy set representation method based on Gaussian membership function for spatial-spectral features is proposed, which describes the inherent fuzziness of the spatial-spectral features. Finally, to perform the fuzzy similarity measure between the fuzzy sets of query samples and prototypes, we construct a spatial-spectral fuzzy metric space, in which HSI pixels with category uncertainty in their features can be better classified under the condition of small-scale labeled samples. Extensive experimental results on three public HSI datasets demonstrate that the proposed DFML method outperforms the state-of-the-art few-shot HSI classification methods.