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Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its limitations in modeling sample relations. Graph convolution network (GCN) has been introduced to HSI classification due to its demonstrated ability in processing sample relations. Introducing GCN into HSI classification, the key issue is how to transform HSI, a typical euclidean data, into non-euclidean data. To address this problem, we propose a supervised framework called the Global Random Graph Convolution Network (GR-GCN). A novel method of constructing the graph is adopted for the network, where the graph is built by randomly sampling from the labeled data of each class. Using this technique, the size of the constructed graph is small, which can save computing resources, and we can obtain an enormous quantity of graphs, which also solves the problem of insufficient samples. Besides, the random combination of samples can make the generated graph more diverse and make the network more robust. We also use a neural network with trainable parameters, instead of artificial rules, to determine the adjacency matrix. An adjacency matrix obtained by a neural network is more flexible and stable, and it can better represent the relationship between nodes in a graph. We perform experiments on three benchmark datasets, and the results demonstrate that the GR-GCN performance is competitive with that of current state-of-the-art methods.

In this study, Zernike polynomials and optical fiber field theory are applied to build a mathematical model of coupling efficiency (CE) and spatial mode of aberrations. The theory built in this paper can be used to quickly calculate the CE affected by a single aberration as well as the aberrations caused by atmospheric turbulence. The aberrations are classified based on Zernike polynomials and the effects of aberrations on CE of different types and different spatial frequencies are analyzed. The influence of the effects of AO system residual errors is also analyzed. Adaptive optics (AO) equipment is applied to build a system on which the proposed theory was tested, the experimental results validate the theoretical analysis.