# GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images
## 5 Results and Discussion  
### Feature Extraction  
As stated above, the choosing rule aims to adaptively determine the number of principal components of feature extraction, which is also the dimension k after dimension reduction. The appropriate selection of \(k\) value allows us to express more information with fewer components. Figure 4 shows the eigenvalue curves of three hyperspectral datasets. The positions of the dotted lines represent the selected \(k\) values of the three datasets under the choosing rule. It shown in Figure 4, that the choosing rules make the selected k of the three datasets fall in the region where the k value is very small and basically does not change, which means that the first k principal components represent enough data information. Then we evaluate the choosing rules from the perspective of classification effect and verify the validity of feature extraction, where we choose CMR-SVM [32] for the classifier. As for the classification effect, several metrics have been used to measure the classification accuracies [33]. Accuracy is essentially a measure of how many ground truth pixels were classified correctly (in percentage), average accuracy (AA) is the average of the accuracies for each class, and overall accuracy (OA) is the accuracy of each class weighted by the proportion of test samples for that class in the total training set. Kappa statistic (Kappa), computed by weighting the measured accuracies, is a measure of agreement normalized for chance agreement. We choose k value starting at 2 (to ensure effective classification) and a final value of 25 (greater than the value determined by the choosing rule), and perform a classification test at each \(k\) value. In order to meet the situation that there are few labels for individual classes in the dataset, we randomly choose five labeled pixels from each class as training samples and used the remaining labeled pixels as test samples. All experiments are repeated ten times and averaged. Figure 5 shows the curve of the three metrics, where the dotted lines are the k value determined by the choosing rule. From the curve, it can be seen that as the k value increases, the three metrics all increase sharply and then tend to be saturated. The \(k\) value determined by the choosing rule is  
Figure 4: Results of the eigenvalue curve of three hyperspectral datasets through choosing rule. \begin{table}
\begin{tabular}{c c} \hline \hline  & **NVIDIA Jetson AGX Xavier** \\ \hline GPU & NVIDIA Volta architecture with 512 NVIDIA CUDA cores \\ \hline CPU & 8-Core Carmel ARM v8.2 64-Bit CPU, 8 MB L2 + 4 MB L3 \\ \hline Memory & 32 GB 256-Bit LPDDR4x 2133 MHz—136.5 GB/s \\ \hline Size & 87 mm \(\times\) 100 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Several key technical specifications of NVIDIA Jetson AGX Xavier module. basically located at the critical point of the saturation region, fully shows that the choosing rules we proposed can make feature extraction adaptively and flexibly. At the same time, we evaluate the classification effect of the proposed algorithm by comparing some other feature extraction and classification methods, i.e., the R-SVM (perform SVM on the original dataset directly), the G-PCA (a GPU parallel implementation of PCA), the MRFs method [34], the LBP-based method [35] and the SC-MK method [36]. The relevant parameters are set to default values as stated above, and the number of principal components of G-PCA is the same as that of INAPC, which is the k value determined by the choosing rule. Similarly, each method was repeated ten times and averaged, and the classification results are shown in Figure 6. Furthermore, Figure 6 shows that R-SVM has the worst results because of the redundant and invalid information in the multi-band. Then after different feature extraction, the classification effects of the other methods are much greater than that of the R-SVM.