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.

翻译：近年来,超光谱图像(HSI)分类研究在引入深网络模型方面不断取得进展,最近,基于图形革命网络(GCN)的模型显示出令人印象深刻的性能;然而,这些基于点估计的深层次学习框架具有较低的概括性,无法量化分类结果的不确定性;另一方面,仅仅应用基于分布估计的巴伊西亚神经网络(BNN)对高光谱图像分类无法达到高分类精确度,因为参数数量众多。在本文件中,我们设计了一个包含巴耶西亚层的想法的Bayesian层,作为基于点估计的神经网络的插入层,并提议采用巴伊西亚图革命网络(BLGCN)模型,该模型可以有效地提取图表信息,并估计分类结果的不确定性。此外,一个Genemental Aversarial网络(GAN)的构建是为了解决HSI数据集的抽样不平衡问题。最后,我们根据分类结果的可信度间隔设计了一个动态控制培训战略,这将在信任度达到预先设定的临界度时尽早结束培训。 实验性结果显示我们模型的准确性能达到总的高度的分类。