S2TNet: Spectral–Spatial Triplet Network for Few-Shot Hyperspectral Image Classification
Guijie Yue, Ling Zhang, Yiyang Zhou, Yuxiang Wang, Zhaohui Xue
Abstract
Deep learning (DL) has shown great potential for hyperspectral image (HSI) classification. However, DL models easily get trapped into overfitting due to limited training samples. To overcome this issue, a novel spectral–spatial triplet network (S2TNet) is proposed for few-shot HSI classification. First, a lightweight spectral–spatial network (SSN) composed of 1-D and 2-D convolution is introduced to extract spectral–spatial features. Second, a hard sample selection strategy is proposed by integrating classification and contrast training to deal with unbalanced positive and negative samples in traditional triplet networks. Third, an enhanced triplet loss function is proposed by considering the relationship between positive and negative sample pairs to ensure the distance between homogeneous samples is smaller than that of heterogeneous samples, which effectively improves the discrimination ability of the model. Experiments conducted on two widely used hyperspectral datasets demonstrate that S2TNet significantly outperforms other related methods, with 0.81%–16.83% and 1.40%–13.83% improvements (under 20 labeled samples per class for training) in terms of overall accuracy (OA) in Indian Pine (IP) data and University of Pavia (PU), respectively.