**4. Experiment**

In this section, the experimental results are performed on three real hyperspectral datasets to evaluate the performance of the proposed ELP-RGF method.

## *4.1. Datasets Description*

In our experiments, three hyperspectral image datasets including the Indian Pines image, the University of Pavia image and the Kennedy Space Center image are utilized to evaluate the performance of the ELP-RGF.

(1) Indian Pines dataset: This image was acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor, which captured an Indian Pines unlabeled agricultural site of northwestern Indiana and contains 220 × 145 × 145 bands. Twenty water absorption bands (Nos. 104–108, 150–163 and 220) were removed before hyperspectral image classification. The spatial resolution of the Indian Pines image is 20 m per pixel, and the spectral coverage ranges from 0.4–2.5 μm. Figure 3 shows a color composite and the corresponding ground-truth data of the Indian Pines image.

(2) University of Pavia dataset: This image capturing the University of Pavia, Italy, was recorded by the Reflective Optics System Imaging Spectrometer (ROSIS). This image contains 115 bands and a size 610 × 340 with a spatial resolution of 1.3 m per pixel and a spectral coverage ranging from 0.43–0.86 μm. Using a standard preprocessing approach before hyperspectral image classification, 12 noisy channels were removed. Nine classes of interest are considered for this image. Figure 4 shows the color composite and the corresponding ground-truth data of the University of Pavia image.

(3) Kennedy Space Center dataset: The Kennedy Space Center (KSC) image was captured by the National Aeronautics and Space Administration (NASA) Airborne Visible/Infrared Imaging Spectrometer instrument at a spatial resolution of 18 m per pixel. The KSC image contains 224 bands with a spatial size of 512 × 614, and the water absorption and low signal-to-noise ratio (SNR) bands

were discarded before the classification. Figure 5 shows the KSC image and the corresponding ground-truth data.

**Figure 4.** University of Pavia image. (**a**) False-color composite; (**b**,**<sup>c</sup>**) Ground-truth data.

**Figure 5.** (**<sup>a</sup>**,**b**) Ground truth data of the Kennedy Space Center images.

#### *4.2. Parameter Analysis of the Proposed Method*

In the experiments, the original images were segmented by the multi-scale segmentation method (MSS) [37]. In this section, we fix the shape parameters *v* = 0.1 and the smoothness parameter to *u*1 = 0.5 in MSS. For the proposed method, there are three hyperparameters that have to be adjusted, namely weight parameter *u*, segmentation scale *S* and the width of spatial neighborhood *d*. The three hyperparameters were selected using the cross-validation strategy. Figure 6 shows the classification results obtained by the ELP-RGF method with different weight parameters *μ* and segmentation scales *S*. From Figure 6, we can see that the result of Figure 6a–b is visually more satisfactory than that of Figure 6c–d. If the process of label propagation entirely relied on the spatial graph, that is, u=1 is applied, the result of ELP-RGF is poor, as Figure 6d shows. Therefore, *μ* = 0.001 to *μ* = 0.01

is considered the most optimal weight parameter range. We can see that the classification result of Figure 6f–g is better than Figure 6e,h, especially for the landscape of "Soybeans-min till" and "Hay-windrowed". In addition, Figure 7 shows the OA curves of ELP-RGF in the different u and dto illustrate the spatial weight parameter playing an important role in the process of label propagation. Furthermore, Figure 8 shows that the classification accuracies and computing time of the proposed method are significantly affected by *S*. When the two factors of the classification accuracy and the computing time are taken into full consideration (see Figure 8) and observing the selected parameters obtained by cross-validation, we can know that the optimal parameter range is 4–6.

**Figure 6.** The analysis for the hyperparameters *μ* and *S* for the Indian Pines image. In the first row, *S* is fixed as five. (**<sup>a</sup>**–**d**) respectively show the classification results obtained by the extended label propagation (ELP)-RGF method with (**a**) u = 0.001, (**b**) u = 0.01, (**c**) u = 0.1 and (**d**) u = 1. In the second row, *μ* is fixed as 0.01; (**<sup>e</sup>**–**h**) respectively show the classification maps obtained by ELP-RGF method with (**e**) *S* = 1, (**f**) *S* = 3, (**g**) *S* = 5 and (**h**) *S* = 9.

**Figure 7.** Influence of *μ* and *d* on the Kappa coefficient of ELP-RGF for the Indian Pines dataset.

**Figure 8.** The effect of the different segmentation size on OA accuracy and computing times for three datasets.

#### *4.3. Comparison with Other Classification Methods*

In this section, the proposed ELP-RGF method is compared with several hyperspectral image classification methods, i.e., the typical SVM method, more advanced extended random walkers (ERW) [43] and semi-supervised methods (the Laplacian support vector machine (LapSVM) [38] and spectral-spatial label propagation (SSLP-SVM) [24]). In addition, a post-processing-based edge-preserving filtering (EPF) [44] and the rolling guidance filtering method (RGF) [39] are also used as the comparison methods. The parameter settings for the EPF, ERW and SSLP-SVM methods are given in the corresponding papers. The evaluation indexes in Tables 1–4 are given in the form of the mean ± standard deviation.

For the Indian Pines dataset, Table 1 shows the OA, AA and Kappa coefficient of different methods with the 5/10/15 training numbers per class (represented as *s*). From Table 1 we can see that the OA accuracy and Kappa coefficient of the proposed ELP-RGF method are better than other methods when the number of training samples is relatively small. In particular, when the *s* = 5, the OA accuracy of the proposed ELP-RGF method increases 14.29% and 36.63% compared to that of the SSLP-SVM method and the LapSVM method. The Kappa coefficient of ELP-RGF method is 13.52% higher than SSLP-SVM when *s* = 10, which fully shows the superiority of the two-step method proposed in this paper. We can see that the performance of the proposed ELP-RGF is always superior to that of the ERW method. As the number of training samples increases, the accuracy of the increase rate has decreased, however, there is still a large gap compared with other methods. Figure 9 shows the classification maps obtained by different methods. It can be seen that the classification map of the proposed method has less noise, and the boundary region in the classification map is also much clearer.


**Table 1.** Comparison of classification accuracies (in percentage) provided by different methods using different training samples per class (Indian Pines image). EPF, edge-preserving filtering; ERW, extended random walkers; LapSVM, Laplacian SVM; SSLP, spectral-spatial label propagation.

For the University of Pavia image, we randomly selected 5, 10 and 15 samples from each class as the training samples. Table 2 shows the OA, AA and Kappa coefficient of the different methods with different s. According to Table 2, the proposed ELP-RGF, SSLP-SVM, LapSVM and ERW can produce greater classification accuracy than the SVM at the same s. However, the degrees of the improvement of ERW, SSLP-SVM and LapSVM are smaller compared with ELP-RGF. For example, the OA of ERW and SSLP-SVM increased by 1.16% and 10.24%. The experimental result indicated that the proposed ELP-RGF outperforms the compared methods. The results show that the OA of the proposed method is 96.02%, which is 10.25% higher than that of the SSLP-SVM and 0.91% higher than that of the ERW when *s* = 15. The OA accuracy and kappa coefficients of the ELP-RGF method are always the highest, which demonstrates that the ELP-RGF is the most accurate classifier among these methods. We can see that compared with SVM, SSLP-SVM and ERW, the OA accuracy and Kappa coefficients of the ELP method are more competitive. Figure 10 shows the classification maps of different methods when *s* = 15. The figure shows the effectiveness of the proposed method. The proposed method presents more accurate classification results for the class of MetalSheetsand Gravel, and its classification result is better than those of other methods.

**Figure 9.** Classification maps of different methods for the Indian Pines image. (**a**) Reference image, (**b**) SVM method; (**c**) EPF method; (**d**) RGF method; (**e**) ERW method; (**f**) LapSVM method; (**g**) SSLP-SVM method and (**h**) ELP-RGF method.

**Table 2.** Overall accuracy of the various methods for the University of Pavia image (average of 10 runs with thestandard deviation; the bold values indicate the greatest accuracy among the methods in each case).


For the Kennedy Space Center dataset, we evaluated the classification accuracies of different methods using 39 training samples collected from each class. Table 3 shows the OAs, AAs, Kappa and individual classification accuracies obtained for the various methods. From Table 3, it is demonstrated that the OA, AA and Kappa accuracy of the proposed method are the highest in all comparative methods. Most of individual accuracies are significantly higher than other methods. For the class of Willowswamp, the accuracies of the proposed method and SSLP-SVM are 99.75% and 77.38%; thus, the accuracy gain is 22.37%. For the class of Oak/Broadleaf, the proposed method can produce 59.21% and 57.11% OA improvements compared with SSLP-SVM and SVM. Table 4 provides the OA accuracies of the various methods. Observing the values in Table 4, we can see that the classification accuracy is proportional to s. Moreover, the performance of the proposed method is not only higher than the other semi-supervised classification methods, but also can improve more than that of ERW. Figure 11 shows that the proposed ELP-RGF method can achieve better classification performance and produce little noise compared with other methods.

**Figure 10.** Classification maps of different methods for the University of Pavia image (**a**) Reference image; (**b**) SVM method; (**c**) EPF method; (**d**) RGF method; (**e**) ERW method; (**f**) LapSVM method; (**g**) SSLP-SVM method and (**h**) ELP-RGF method.

**Table 3.** Individual class accuracies, OA, AA and Kappa coefficient (in percentage) for the Kennedy Space Center images.


**Table 4.** Overall accuracy of the various methods for the Kennedy Space Center image (average of 10 runs with thestandard deviation; the bold values indicate the greatest accuracy among the method in each case).


**Figure 11.** Classification maps of different methods for the Kennedy Space Center image. (**a**) Reference image; (**b**) SVM method; (**c**) EPF method; (**d**) RGF method; (**e**) ERW method; (**f**) LapSVM method; (**g**) SSLP-SVM method; (**h**) ELP-RGF method.

Table 5 lists the number of samples generated by the two semi-supervised methods (i.e., SSLP-SVM and ELP-RGF) under three different datasets and the correct rate of these new labeled samples. We can see that the total number of labeled samples generated by the ELP-RGF method is almost 7–22-times more than that generated by the SSLP-SVM method for three datasets. Although the correct rate of the SSLP-SVM method is slightly higher, the ELP-RGF method is also competitive. More importantly, the proposed ELP-RGF can produce more labeled samples.

Table 6 illustrates the effect of the superpixel by comparing with the RGF method, the combination of label propagation and RGF and the combination of superpixel propagation and RGF. The table shows that the superpixel propagation plays a major role in the proposed method. For example, for the Indian Pines image, the OA accuracy of the proposed ELP-RGF is 79.13%, while the accuracy obtained by SP-RGF and RGF is 75.62% and 56.14%, respectively. For the Kennedy Space Center image, the OA accuracy of the SP-RGF method is 4.64% higher than that of LP-RGF. As Table 6 shows, the accuracy of the SL-RGF is more than that of the LP-RGF method when RGF is used in those methods. While LP-RGF is higher than SP-RGF, the gap is small. Thus, the process of superpixel propagation is very useful to help improve the classification result.


**Table 5.** Correct rate of samples generated by the two semi-supervised methods of three datasets.

**Figure 12.** The process of modifying the wrongly-labeled samples. (**a**) The five wrongly-labeled pseudo-labeled samples are provided; (**b**) shows that the first and second labels of pseudo-labeled have been modified by superpixel propagation; (**c**) shows the real labels of the provided wrongly-labeled pseudo-labeled samples.

**Table 6.** Overall accuracy of the various combined methods involved in the proposed method for three datasets.

