Self-Supervised Spectral–Spatial Graph Prototypical Network for Few-Shot Hyperspectral Image Classification
Shan Ma, Lei Tong, Jun Zhou, Jing Yu, Chuangbai Xiao
Abstract
In recent years, deep learning has been widely applied to hyperspectral image (HSI) classification with great success. However, since labeling hyperspectral images is time-consuming and labor-intensive, a limited number of labeled hyperspectral images are available, making it difficult to train feature extractors and classifiers. To address this challenge, this paper introduces a self-supervised spectral-spatial graph prototypical network for few-shot HSI classification (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> GPN). In addition, we combine self-supervised learning with few-shot learning to provide additional semantic information to improve classification accuracy. Our method consists of three stages, including the prototype network (PN) stage, the self-supervised learning (SSL) stage, and the fusion stage (Fusion stage), with each stage progressively improving the classification performance. In the PN stage, we perform supervised learning using a prototype network structure and leverage the supervised information to guide self-supervised learning. In the SSL stage, we use the SimSiam structure in a novel way for model training after the augmentation of spectral and spatial data separately. Finally, the features learned in the first two stages are fused to improve the quality of the feature representation. These three stages use the same structured feature extractors. To extract more diverse and discriminative feature representations for the HSI classification task, our method uses the graph convolution network (GCN) and the dense network (Densenet) to extract spectral information and spatial information, respectively. Experiments on four data sets and comparisons with the state-of-the-art methods demonstrate that our proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> GPN outperforms other methods for HSI classification.