*4.2. Experimental Settings*

To evaluate the performance of the SVM-SSHK method, seven state-of-the-art kernel-based classification methods were selected for comparison, including SVM, EMP [28], EPF [27], SVM using a composite kernel (SVM-CK) [20], MLR-GCK [45], the two superpixel-based classifiers using spectral-spatial kernel (SC-SSK) [22], and multiple kernels (SC-MK) [46]. The overall accuracy (OA), average accuracy (AA), and kappa coefficient (*κ*) were used for quantitative evaluation. Before demonstrating the experimental results, a brief description on the parameter settings and related issues are provided. To fix the optimal parameter settings for each method, we tuned these parameters in a certain range based on the original references to obtain the best classification performance, which can be comparative to the classification results from these original references for the IP and UP images with the same number of training samples. The parameter settings for each method are provided as follows:

(1) The SVM algorithm with the RBF kernel was exploited by all of the methods, except for MLR-GCK, and the optimal *C* and *γ* for each method were obtained by five-fold cross validation ranging from 2−<sup>5</sup> to 2<sup>15</sup> and 2−<sup>15</sup> to 25, respectively.


**Figure 3.** Hyperspectral images and the corresponding ground truth data (GTD). (**a**) A false color composite image (bands 47, 23, and 13) of the Indian Pines (IP) image and (**b**) its GTD; (**c**) a false color composite image (bands 103, 56, and 31) of the University of Pavia (UP) image and (**d**) its GTD.

In our experiments, we randomly divided the GTD for training and test and followed the scheme in [46] by setting training samples *M* ranging from 15 to 40 with a step size of 5 for each class and the rest for test. For some minority classes in the IP image, the labeled samples were divided into the equal training and test samples when the total of the labeled samples is less than *M*. Table 1 demonstrates that the percentage of the total samples (pixels) that were used for training and test for the two HSIs under different values of *M*. The classification experiments using each training set were repeated 10 times for reliable evaluation of the results.


**Table 1.** The percentage of the total pixels used as training and test for the IP and UP images under different values of *M*.
