## 4 Experiment Results and Analysis
### Comparison of other Proposed Methods  
To verify the feasibility of the proposed method, we compare some other CNN-based methods proposed in recent years on the Salinas and Pavia University datasets, including the methods CNN in [26], CNN-PPF in [46] and CD-CNN in [47]. The architecture of the classifier, proposed by [PERSON] et al., comprises an input layer, the convolutional layer, the max poolinglayer, the fully-connected layer, and the output layer with weights [26]. In paper of [PERSON] et al., a pixel-pair method was proposed to markedly increase such a number. This will enable the advantages provided by CNN to be used as much as possible. For testing pixels, the trained CNN classifies the pairs of pixels created by combining the central pixel with each surrounding pixel, and then determines the final label through voting strategy [46]. In the paper by [PERSON] et al. [47], a deep CNN, which was deeper and wider than any other deep network for HSI classification was described. Different from methods in CNN-based hyperspectral image classification, the proposed network--a contextual deep CNN--can best explore local contextual interactions, by jointly utilizing local spatial-spectral relationships of neighboring individual pixel vectors. By using a multi-scale convolution filter bank as the initial component of the proposed CNN pipeline, the joint development of spatial-temporal spectral information can be achieved. After that, the original spatial and spectral feature maps obtained from the multi-scale filter bank are combined together to form a joint spatial-spectral feature map that represents abundant spectral and spatial properties of the hyperspectral image. The joint feature map is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. In this experiment, 50, 100, 150, and 200 training samples for each class are set respectively. The overall accuracy is shown in Table 6. As it can be seen in the Table, when the number of training samples increases, the overall accuracy also increases. In the case of the same number of training samples, it is clear that the proposed method almost always outperforms other three methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Numbers of Training Samples**} \\ \cline{3-6}  & & 50 & 100 & 150 & 200 \\ \hline \multirow{3}{*}{Salinas} & CNN [26] & 89.20 & 89.58 & 89.60 & 89.72 \\  & CNN-PPF [46] & 92.15 & 93.88 & 93.84 & 94.80 \\  & CD-CNN [47] & 82.74 & **98.58** & - & 95.42 \\  & Proposed method & **92.18** & 93.77 & **95.02** & **96.41** \\ \hline \multirow{3}{*}{Pavia} & CNN [26] & 86.39 & 88.53 & 90.89 & 92.27 \\  & CNN-PPF [46] & 88.14 & 93.35 & 94.97 & 96.48 \\ \cline{1-1}  & CD-CNN [47] & 92.19 & 93.35 & - & 96.73 \\ \cline{1-1}  & Proposed method & **94.34** & **96.25** & **97.64** & **97.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall accuracy (%) versus different numbers of training samples per class for different methods. The best results are highlighted in bold. Figure 10: Effect of the number of training samples for the Salinas and Pavia University datasets in the spectral–spatial method. Sa and Pa represent the Salinas and Pavia University datasets respectively. S, S-P and S-S represent for methods based on spectral, spectral + PCA and spectral–spatial + PCA. We can see from the overall accuracy of the Salinas dataset, with 50 training samples in each category, the overall accuracy of the proposed method is 92.18%. The overall accuracy resulting from the method proposed is 9.44%, higher than the lowest one. With 100 training samples in each category, the overall accuracy of the proposed method is not the maximum. With 150 training samples in each category, the overall accuracy of the proposed method is 95.02%. The overall accuracy resulting from the method proposed is 5.42%, higher than the lowest one.