Fuzzy Triple Contrastive Learning for Hyperspectral Image Classification

Recently, contrastive learning (CL) has shown excellent performance in hyperspectral image (HSI) classification. However, existing CL based methods face two specific challenges. (1) Multi-view samples inevitably introduce ambiguity and uncertainty due to data augmentation operations. Traditional contrastive learning methods fail to effectively model these dynamic ambiguous features, resulting in a lack of robustness in the feature learning process. (2) Existing CL based methods primarily learns feature representations by pulling positive samples closer and pushing negative samples apart. But, they lack structured modeling of intra-class feature compactness and inter-class feature separability. To address these challenges, we propose a fuzzy triplet contrastive learning (FTCL) method for HSI classification. For the first challenge, we propose a multi-view fuzzy neighborhood learning (MFNL) module. This module effectively models the ambiguity among multi-view samples through fuzzy membership calculation, multi-view fuzzy weight matrix generation, and weighted feature aggregation, significantly enhancing the robustness and stability of feature representations. To tackle the second challenge, we design a triplet feature discriminative (TFD) classifier, which improves intra-class compactness by minimizing the distance between anchor samples and positive samples, while enhancing inter-class separability by maximizing the distance between anchor samples and negative samples. This enables precise modeling of intra-class compactness and inter-class separability. The proposed method is evaluated on four HSI datasets, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.