## 5 Results
### Results from the Simulation Dataset
#### 5.2.1 Analysis for SNR = 1000  
The pixel-based evaluations used in HSI classification are the Product's Accuracy (PA)--that is, the ratio of the number of correctly classified pixels of a certain class to the total number of samples of that class. Overall accuracy (OA): the ratio of the number of correctly classified pixels to the total number of pixels. Average accuracy (AA): the sum of the production accuracy of each class divided by the number of categories. Kappa coefficient: measure the consistency between the classification result map and the real marked map. Table 3 shows the classification results of the three wavelength selection algorithms on the simulated data set under the conditions of SNR of 1000, and Figure 11 is the corresponding visual classification diagram. In order to avoid unstable results, each algorithm repeats the dataset 10 times and displays its average accuracy (AVG) and standard deviation (STD). The best results of the three algorithms in the table are highlighted, and SVM is the original wavelength classification result. It can be seen that the SVM classification results using the original 301-dimensional wavelengths only reached a high level for the classification of vegetation and camouflage cloth, while the accuracy of green coating and artificial leaves and the other three classification evaluations of OA, AA and Kappa coefficients \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Result Dimension** & **Result** \\ \hline GA-BPSO & 39 &
\begin{tabular}{c} (549, 551, 552, 554, 557, 559, 560, 601, 602, 604, 605, 607, 642, 643, 659, 662, 665, 668, 669, 670, 671, 672, 679, 680, 681, 682, 683, 685, 689, 690, 691, 692, 693, 702, 703, 704, 705, 710, 712) \\ \hline IOIF & 10 & (499, 500, 501, 502, 605, 607, 642, 726, 734, 735) \\ \hline Partitioned Relief-F & 14 & (493, 497, 553, 552, 556, 554, 669, 668, 699, 698, 716, 715, 750, 749) \\ \hline LBI-BPSO & 10 & (501, 515, 521, 528, 706, 723, 729, 732, 744, 749) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensionality reduction results. are much lower than the results after dimensionality reduction. This demonstrates the necessity of hyperspectral image DR for classification work. Among the four DR methods, the LBI-BPSO method proposed in this paper obtains a smaller number of wavelengths, and is superior to GA-BPSO, IOIF and Partitioned Relief-F in the classification accuracy of various ground objects, OA, AA and Kappa coefficients. Due to the limitation of search performance, the final result of GA-BPSO is 39 wavelengths, and the feature dimension exceeds that of IOIF and LBI-BPSO. From the perspective of classification performance, redundant information leads to a decrease in classification accuracy, so the GA-BPSO algorithm that only uses inter-class separability for optimal combination search is the worst. However, LBI-BPSO makes the search space focus on the characteristic region with a large amount of information through preliminary screening of the wavelengths containing the largest amount of information, and further wavelength selection is carried out with the goal of maximizing the inter-class distance in this region. Finally, LBI-BPSO successfully reduces the initial 301-dimensional feature space to 10-dimensional. The classification effect of LBI-BPSO and Partitioned Relief-F is very close, and LBI-BPSO shows a small advantage. Compared with IOIF, LBI-BPSO classification results show that OA is improved by 2.90%, AA is improved by 2.75%, and Kappa is improved by 3.91. Compared with GA-BPSO, OA is increased by 3.17%, AA is increased by 3.19%, and Kappa is increased by 4.94. The above proves that the LBI-BPSO DR method combining the two DR criteria of information content and inter-class separability is better than IOIF based on information content alone and GA-BPSO considering only inter-class separability in terms of classification effect. Figure 11 shows the result of the classification visualization.