## 4 Experiment Results and Analysis
### The Performance of Classification on the Salinas and Pavia University Datasets  
In this part, the three methods based on spectral, spectral + PCA, and spectral-spatial + PCA are compared. Among them, the method based on spectral does not carry out PCA preprocessing on the original hyperspectral image, but only normalization. Therefore, each pixel contains all the spectral information of the original image, and such a pixel containing all the original spectral information is taken as a sample. For the method based on spectral + PCA, PCA preprocessing is carried out after normalization, and then the first \(c_{r}\) principal components are selected to reconstruct the image. When extracting the pixel, it does not consider the information of the neighborhood pixel, so each pixel only contains \(c_{r}\) components of the pixel. In this experiment, \(c_{r}\) is set to 25. Spectral-spatial + PCA is the method proposed in this paper. The label maps of ground truth were shown in Figures 8a and 9a and the classification maps were shown in Figures 8b-d and 9b-d. It has to be mentioned that the black background pixels were not considered for our classification purpose. The classification results including OA, AA, Kappa, and time were displayed in Tables 4 and 5, and the best results for each category are highlighted in bold. Space spectral combination method based on PCA: This method is proposed in this paper. Firstly, PCA dimension reduction is carried out on the original hyperspectral image, and then the  
Figure 6: Different kernels in the first convolutional layer of the CNN model, in which the symbol of ‘+’ represents for concatenation. (**a**): Convolution kernels are 1*1@12; (**b**): Concatenation of the two scale convolution kernels of 1*1@6+3*3@6; (**c**): Concatenation of the three scale convolution kernels of 1*1@4+3*3@4+5*5@4. Figure 7: The convergence by different kernels. (**a**) Tested on the Pavia University dataset, (**b**) tested on the Salinas dataset. information of the target pixel and all pixels in its neighborhood are extracted as sample data for training and classification. Tables 4 and 5 show that the proposed method almost obtains optimal performance across all categories, and it also displays the best classification performance compared with the other two methods in terms of OA, AA, and Kappa. The proposed method is about 6% to 12% higher than the other two methods in both datasets in terms of OA, showing a great improvement in HSI classification. Considering AA, the method proposed is about 4.3% to 12%, higher than the other two methods in both datasets. We can see classification accuracy from the kappa coefficient. The proposed method outperforms the other two methods. Moreover, the proposed method has 100% classification accuracy in class 1, 2, 3, and 6 in the Salinas dataset and in class 5 in the Pavia University dataset. Visually, as shown in Figures 8 and 9, the noisy points are greatly decreased in spectral-spatial based on PCA method. The reason why the proposed method can make such a great improvement is that it can compensate the insufficient of spectral information only by utilizing the spatial dependence of pixels. Figure 8: Classification maps for the Salinas dataset. (**a**) Label map; (**b**) method based on spectral; (**c**) method based on spectral + PCA; (**d**) method based on spectral–spatial + PCA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Spectral** & **Spectral + PCA** & **Spectral-Spatial + PCA** \\ \hline
1 & 96.17 & 99.75 & **100.00** \\
2 & 99.81 & 99.87 & **100.00** \\
3 & 99.75 & 96.96 & **100.00** \\
4 & 99.21 & 99.21 & **99.93** \\
5 & 98.36 & 98.32 & **98.58** \\
6 & 99.77 & 99.70 & **100.00** \\
7 & 99.64 & 99.61 & **99.80** \\
8 & 70.00 & 87.19 & **91.40** \\
9 & 99.03 & 99.15 & **99.97** \\
10 & 93.90 & 92.01 & **97.28** \\
11 & 95.97 & 98.97 & **99.81** \\
12 & 99.74 & 96.16 & **99.95** \\
13 & 98.47 & 99.56 & **99.67** \\ \hline \hline \end{tabular}
\end{table}