Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean data sets. To solve this problem, recently, graph convolutional neural network (GCN) is proposed, which can be applied to the semi-supervised HSI classification problem. However, the GCN is a direct push learning method, which requires all nodes to participate in training process to get the node embedding. This may bring great computational for the hyperspectral data with large number of pixels. Therefore, in this paper, we propose a inductive learning method to solve the problem. It constructs the graph by sampling and aggregating (graphSAGE) feature from a node's local neighbourhood. This could greatly reduce the space complexity. Moreover, to enhance the classification performance, we also construct graph using spectral and spatial information (spectral-spatial GraphSAGE). Experiments on several hyperspectral image datasets show that the proposed method can achieve better classification performance compared with state-of-the-art HSI classification methods.