## 4 Results and Discussion
### Waveband Processing  
Hyperspectral image data contain hundreds of consecutive spectral bands that provide rich spectral and spatial information [46]. However, a higher number of bands leads to increased inter-band correlation, data redundancy, and computational costs, which can result in the Hughes phenomenon [47]. Therefore, reducing the dimensionality of data is necessary. However, the choice of dimensionality reduction method can affect the experimental results. In this experiment, the within-band variance-based combined with Frobenius norm\({}^{2}\)[48] (F-norm\({}^{2}\)) algorithm was compared with a principal component analysis (PCA), a standard dimensionality reduction algorithm for hyperspectral images, to select the most accurate classification result among the initial network model processing methods. Variance [49] is typically used to describe the degree of deviation among data points in a random variable. F-norm\({}^{2}\) is used to describe the different distances between unrelated n-dimensional variables. In this experiment, the variance value for each spectral band was used to describe the degree of the dispersion of the information content among the spectral bands. The more significant the difference in variance values, the more dispersed the information. The F-norm\({}^{2}\) value describes the amount of information in each spectral  
Figure 9: Streamlined 2D-CNN model. band. The larger the F-norm\({}^{2}\) value, the richer is the information content. Equation (7) for calculating the variance is:  
\[S^{2}(x)=\frac{\sum\limits_{l=1}^{N}\left[x_{l}-\mu\right]^{2}}{N} \tag{7}\]  
Equation (7), where: \(S^{2}(x)\) indicates the band variance, \(N\) indicates the number of pixels in a single band, \(x_{i}\) indicates the pixel value, and \(\mu\) indicates the mean of the pixel values in a single band. The F-norm\({}^{2}\) calculates the equation shown in (8):  
\[\parallel X(\text{; ; ; b})\parallel F=\left(\sum\
olimits_{r}^{R}\sum \
olimits_{c}^{C}\left|X(r,c,b)\right|^{2}\right)^{\frac{1}{2}} \tag{8}\]  
Equation (8), where: \(X\) is the tensor, \(r\) is the number of rows (samples), \(c\) is the number of columns (lines), and \(b\) is the number of bands (bands). Figure 9(a) schematically shows the normalized F-norm\({}^{2}\) values and Figure 9(b) the results of the within-band variance operation. Figure 9(a) shows that, before 677 nm (the number of bands is 96) and after 751 nm (166 band), although the number of bands increases further, the value decreases. In both cases, the number of intermediate bands was relatively small, but the value increased sharply. This indicated that the information content increased sharply at this time. In Figure 9(b), there is a decline from 689 nm (126 band) to 713 nm (136 band), which is a turnaround compared to the bands before and after, indicating that the information in this band was relatively stable and concentrated. In summary, band division was conducted by choosing 126-136 bands as the center and the left and right bands as the increments. The experimental results are listed in Table 2.  
Table 2 shows that all four categories of bands achieved a better performance, and the training time increased with the increase in bands. The first and the fourth categories had a greater classification accuracy, but the overall accuracy difference was insignificant. Regarding time costs and the redundancy of the band information, the first category should be selected as the input band for the subsequent model. Under the same training condi \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Selection of Different Wave Numbers**} \\ \hline Class & 1 & 2 & 3 & 4 & PCA & Full-band \\ Bands & 126–136 & 116–146 & 106–156 & 96–166 & 11 & 256 \\ Test loss (\%) & 0.022 & 0.023 & 0.024 & 0.020 & 0.026 & 0.051 \\ Kappa \(\times\) 100 & 98.735 & 98.637 & 98.617 & 98.788 & 98.524 & 96.858 \\ OA (\%) & 99.216 & 99.156 & 99.143 & 99.249 & 99.085 & 98.053 \\ Train time (s) & 367 & 382 & 383 & 471 & 427 & 585 \\ \hline \hline \end{tabular}
\end{table}