  In recent years, research on hyperspectral image (HSI) classification has
continuous progress on introducing deep network models, and recently the graph
convolutional network (GCN) based models have shown impressive performance.
However, these deep learning frameworks based on point estimation suffer from
low generalization and inability to quantify the classification results
uncertainty. On the other hand, simply applying the Bayesian Neural Network
(BNN) based on distribution estimation to classify the HSI is unable to achieve
high classification accuracy due to the large amount of parameters. In this
paper, we design a Bayesian layer with Bayesian idea as an insertion layer into
point estimation based neural networks, and propose a Bayesian Layer Graph
Convolutional Network (BLGCN) model by combining graph convolution operations,
which can effectively extract graph information and estimate the uncertainty of
classification results. Moreover, a Generative Adversarial Network (GAN) is
built to solve the sample imbalance problem of HSI dataset. Finally, we design
a dynamic control training strategy based on the confidence interval of the
classification results, which will terminate the training early when the
confidence interval reaches the preseted threshold. The experimental results
show that our model achieves a balance between high classification accuracy and
strong generalization. In addition, it can quantifies the uncertainty of the
classification results.
