**5. Discussion**

In this paper, the proposed ELP-RGF method is used to increase the number of training samples and optimize the features of the initial hyperspectral image. Previous label propagation-based works, such as SSLP-SVM, only increased a small number of training samples, which are neighboring the labeled samples, and the computational expense is large. If the scope of propagation is beyond neighbors, the computing time will rapidly increase. Furthermore, in the process of label propagation, some wrongly-labeled samples may be introduced to train the model, resulting in misclassification. In our ELP-RGF method, a two-step label prorogation process called ELP is proposed, which first utilized the spatial-spectral label propagation to propagate the label information from labeled samples to the neighboring unlabeled samples. Then, superpixel propagation is used to expand the scope of propagation to the entire superpixel to increase the huge number of training samples, and it is less time consuming compared to the propagation beyond neighbors. Compared with other semi-supervised classification methods, ELP has two obvious advantages: on the one hand, it can generate a large number of pseudo-labeled samples for model training; on the other hand, it can ensure the 'effectiveness' of the increased pseudo-labeled samples; here, 'effectiveness' means that almost all of the labels of the pseudo-labeled samples are correct, which was shown in Table 6. Moreover, as shown in Figure 12, the wrongly-labeled samples in the first step of the ELP method can be modified by the superpixel propagation. Thus, the proposed ELP-RGF method shows a better classification performance than other comparative methods. However, the greatest limitation of the proposed method is that the classification result is over-reliant on the segmentation scale. As shown in Figure 8, the difference in classification results with different segmentation scales is larger.
