The classification of hyperspectral images with a paucity of labeled samples is a challenging task. This paper describes the use of a superpose probability matrix and weight matrix of an L1 graph, thereby forming a strong discriminating DL1 graph. Combining the local information of the space with the global information of the spectrum through the superposition of a KNN graph and a DL1 graph, a graph-based framework is built that combines the spatial and spectral information. This framework of a DL1KNN graph can reflect the more sophisticated structure of hyperspectral image data. Experimental results show that the improvement in classification accuracy is significant when the percentage of labeled samples is 5% through the use of the label propagation of the graph to achieve semi-supervised classification for improving the automatic classification accuracy of hyperspectral data with a small number of samples.