# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 3 Results
### Detailed Results of State-of-the-Art Networks  
All methods were tested five times, and the results were averaged from the three study areas, of which, the OA, AA, and kappa detailed statistics are listed in Table 3.  
Compared with SVM, DBMF improved the recognition rate by 48% (Tables 3 and 4). The SVM produced the worst classification (0.43), whereas DBMF produced the best result (0.91) among the methods tested because it associated spatial and spectral neighborhood information of the same pixel, unlike SVM, which used them independently. The OA of CD-CNN was the worst among the methods, as 2D-CNN did not integrate spatial and spectral information. Compared with DBMA, which extracts two types of features \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**CD-CNN**} & \multicolumn{3}{c}{**DBMA**} & \multicolumn{3}{c}{**DBDA**} & \multicolumn{3}{c}{**DBMF**} \\ \cline{2-13}  & **HJ1** & **Sen2** & **HJ +** & **HJ1** & **Sen2** & **HJ +** & **Sen2** & **HJ1** & **Sen2** & **HJ +** & **Sen2** & **HJ +** & **Sen2** & **HJ +** & **Sen2** & **HJ +** \\ \hline OA & 40.67 & 36.31 & 42.60 & 47.64 & 44.35 & 48.66 & 62.13 & 60.43 & 63.05 & 81.67 & 81.57 & 82.12 & 84.32 & 82.62 & 90.84 \\ \hline AA & 36.39 & 34.58 & 36.76 & 44.38 & 42.31 & 46.72 & 56.79 & 55.60 & 58.32 & 80.18 & 78.98 & 82.09 & 83.60 & 82.31 & 90.16 \\ \hline Kappa & 31.12 & 30.89 & 33.01 & 43.56 & 42.08 & 45.19 & 50.94 & 54.27 & 55.84 & 77.32 & 72.15 & 81.37 & 83.18 & 84.91 & 90.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy (%) comparison of three areas. Figure 7: Accuracy with the proportion of training samples. independently and employs an attention mechanism, DBMF increased the OA by 27%. The DBMF had better classification effects by extracting multiple-scale information on MSIs and HSIs compared with the DBDA method, which has an improved activation function but does not prevent overfitting. As shown in Tables 5-8, the recognition rate of CD-CNN, DBMA, and DBDA models was low, except for the DBMF model. In general, the classification for spruce was difficult for the models, but the recognition rate of DBMF was higher than the other models for spruce, which also improved the OA. The classification performance of the SVM model was the worst, which only recognized larch and birch so that almost no other tree species were recognized. Obviously, there were defects in high-dimensional and large batch data. For instance, CD-CNN incorrectly classified spruce into larch, the percentage of this almost reached 75%, while the recognition rate of spruce was zero. The recognition rate of DBMA for willow was 0.61, which was better than CD-CNN. In addition to the equivalent effect of spruce, the recognition ability of other tree species was slightly stronger than CD-CNN. As shown in Tables 6 and 7, the recognition rate of DBDA for willow was 0.9, which was better than DBMA, but it was not significantly enhanced for spruce. The classification average accuracy of the proposed DBMF for six tree species was found to generally be better than the other compared algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Species** & **Species Code** & **0** & **1** & **2** & **3** & **4** & **5** & **Recall (\%)** & **F-Score (\%)** \\ \hline Birch & 0 & 4623 & 7142 & 0 & 0 & 0 & 0 & 39.29 & 35.72 \\ \hline Larch & 1 & 4280 & 35,357 & 0 & 0 & 0 & 0 & 89.20 & 70.13 \\ \hline Spruce & 2 & 4334 & 14,268 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Mongolia & 3 & 812 & 2531 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Willow & 4 & 45 & 1003 & 0 & 0 & 0 & 0 & 0 \\ \hline Poplar & 5 & 22 & 884 & 0 & 0 & 0 & 0 & 0 \\ \hline  & Precision & 32.7501 & 57.78 & 16.66 & 16.66 & 16.66 & 16.66 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The tree classification confusion matrix for tree species classification using ground truth (rows) compared to the SVM model species prediction (columns; numbered labels refer to species numbers shown in row labels).