**3. Proposed Method**

Figure 2 shows the schematic diagram of the proposed semi-supervised classification method based on extended label propagation and rolling guidance filtering for the hyperspectral image, which consists of the following steps: First, the extended label propagation method is used to obtain an effective set of pseudo-labeled samples. This step is a two-step process. The first step is that the neighboring unlabeled samples from initial labeled samples are assigned labels by using the graph-based spatial-spectral label propagation method. The second step is that all pixels within the superpixel to which the labeled samples belong are assigned the same labels to achieve further label propagation. Then, pseudo-labeled samples with confidence less than the constant threshold will not be added into the training sample set. Then, rolling guidance filtering is used to optimize the feature of the original image, and the filtered result is used to extend the feature vector that is an input to the SVM. Finally, the initial labeled samples and pseudo-labeled samples are merged in training by SVM.

**Figure 2.** Schematic of the proposed semi-supervised classification method of hyperspectral images based on extended label propagation and rolling guidance filtering.

The proposed semi-supervised classification method based on extended label propagation and rolling guidance filtering (ELP-RGF) method can be shown by Algorithm 1:

**Algorithm 1:** proposed ELP-RGF method


Note that we perform the SVM to obtain the final classification result, because it has a good performance for the non-linear problem [41]. The goal of SVM is to find an optimal decision hyperplane that can maximize the distance between the two nearest samples on the two sides of the plane for classification. In this paper, the "one against rest" strategy [42] is adopted to achieve the multi-classification.
