# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 3 Results
### Detailed Results of State-of-the-Art Networks
\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 & 8036 & 3423 & 47 & 259 & 0 & 0 & 68.30 & 62.98 \\ \hline Larch & 1 & 2972 & 35,991 & 158 & 515 & 1 & 0 & 90.8 & 77.67 \\ \hline Spruce & 2 & 1860 & 11,217 & 5078 & 447 & 0 & 0 & 27.29 & 42.24 \\ \hline Mongolia & 3 & 705 & 1030 & 140 & 1468 & 0 & 0 & 43.91 & 48.15 \\ \hline Willow & 4 & 19 & 688 & 5 & 11 & 325 & 0 & 31.01 & 47.3 \\ \hline Poplar & 5 & 159 & 682 & 11 & 54 & 0 & 0 & 0 & 0 \\ \hline  & Precision & 58.43 & 67.86 & 93.36 & 53.30 & 99.69 & 16.66 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The tree classification confusion matrix for tree species classification using ground truth (rows) compared to the CDCNN model species prediction (columns; numbered labels refer to species numbers shown in row labels). Figure 8 illustrates the tree species classification maps with different methods on the multi-source datasets of three areas. The quality of the DBMF classification map is significantly higher than those of other methods and has the clearest boundaries among them. Furthermore, DBMF readily recognizes tree species while the other methods do not, where the OA reached 95.2%. The accuracies of DBMF for each tree species were 93.5% (birch), 90.8% (larch), 92.5% (mongolica), 86.7% (poplar), 85.9% (spruce), and 91.1% (willow). The DBMF method consistently outperformed other methods in feature mapping, suggesting that the fusion strategy is preferable to operations on single-source data. The DBMA and DBDA methods can simultaneously extract spectral and spatial information, but DBMF presents a better classification effect than both, yielding results closer to the \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 & 8592 & 2400 & 420 & 341 & 0 & 12 & 73.03 & 69.55 \\ \hline Larch & 1 & 3140 & 35,666 & 317 & 475 & 0 & 39 & 89.98 & 80.59 \\ \hline Spruce & 2 & 632 & 8501 & 9301 & 149 & 0 & 19 & 50 & 64.91 \\ \hline Mongolia & 3 & 562 & 1267 & 0 & 1511 & 0 & 3 & 45.19 & 51.70 \\ \hline Willow & 4 & 0 & 701 & 14 & 26 & 307 & 0 & 29.29 & 45.31 \\ \hline Poplar & 5 & 16 & 336 & 0 & 0 & 0 & 554 & 61.14 & 72.27 \\ \hline  & Precision & 66.38 & 72.97 & 92.52 & 60.39 & 99.9 & 88.35 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The tree classification confusion matrix for tree species classification using ground truth (rows) compared to the DBMA model species prediction (columns; numbered labels refer to species numbers shown in row labels). \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 & 10,817 & 710 & 18 & 219 & 0 & 1 & 91.94 & 79.25 \\ \hline Larch & 1 & 3902 & 35,259 & 79 & 356 & 2 & 39 & 88.95 & 86.96 \\ \hline Spruce & 2 & 390 & 4966 & 13,002 & 244 & 0 & 0 & 69.89 & 81.97 \\ \hline Mongolia & 3 & 394 & 327 & 10 & 2612 & 0 & 0 & 78.13 & 76.22 \\ \hline Willow & 4 & 0 & 134 & 10 & 79 & 825 & 0 & 78.72 & 88 \\ \hline Poplar & 5 & 30 & 58 & 0 & 0 & 0 & 818 & 90.28 & 92.74 \\ \hline  & Precision & 69.63 & 85.05 & 99.1 & 74.41 & 99.75 & 95.33 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The tree classification confusion matrix for tree species classification using ground truth (rows) compared to the DBDA model species prediction (columns; numbered labels refer to species numbers shown in row labels).