## 3 Results
### Area and Digital Number for Each Forest Type Predicted by the Various Models  
To further evaluate classification, the confusion matrix was applied to the test dataset. All evaluation metrics (Table 1) showed the same tendencies in terms of the percentages of correctly classified forest areas of each type (Table 2). NMX and CP forests tended to be more susceptible to be classified as NBL forest, whereas the misclassification rates between NMX and CP were very low. Taking MSG-GCN as an example, NMX was more likely to be classified as NBL than CP, while CP was more likely to be classified as NBL than NMX. U-Net and U-Net++ showed the same tendencies as MSG-GCN. There were very few misclassifications of BG points as other forest types; such errors were confined to the edges because of the inevitable errors in raster pixel values. The RF model could not detect NMX forest and tended to misclassify NMX as CP rather than NBL.  
Figure 4 shows that NMX, NBL, and CP forests correctly classified by the four models had very similar mean DN values for the three RGB bands. LSD analysis revealed that the mean DN of NBL was higher than that for CP (Figures 4 and 5) and that the DNs for correctly classified and misclassified forest types were very similar. The distributions of the correctly classified and misclassified forest types overlapped greatly in terms of the DNs, as shown in the plot figures (Figure 4).  
Figure 4: Mean digital numbers (DNs) of forest types correctly classified and misclassified by different models.  
Figure 5: Least significant difference (LSD) analysis of the digital numbers (DNs) of the various bands for different forest types and different models.  
The differences between the DNs of forest types correctly classified and misclassified by the four models were analysed using the LSD test. In terms of the DNs of correctly classified forest types, NMX(c) and NBL(a) differed significantly. In terms of the DNs of misclassified forest types, taking the red band of the MSG-GCN model as an example (Figure 5), a significant difference between NBL(a) and NBL-NMX(bc) was shown, while there was no significant difference between NBL-NMX(bc) and NMX(c). There was no significant difference between CP(ab) and CP-NBL(bc), but there was a difference between CP-NBL(bc) and NBL(a). There was no significant difference between NMX(c) and NMX-NBL(c), but there was a difference between NMX-NBL(c) and NBL(a).  
In terms of the prediction maps (Figure 6) for the test data set, severe salt-and-pepper noise was apparent in the RF map, but this was less severe in the U-Net and U-Net++ maps. However, there were patches where NBL and NMX areas were misclassified as CP or confused with each other. Moreover, the forest type edges were unclear, especially for NMX in U-Net and U-Net++, whereas the MSG-GCN map showed fewer misclassified patches and the edge areas between different forest types were sharper (Figure 6).  
Figure 6: Ground truths of the three forest types. Example of the limitations of spectral classification (i.e., RF) and the U-Net, U-Net++, and MSG-GCN spectral–spatial classification methods. (a) a patch tile contains NMX, NBL and CP forests, (b) a patch tile contains NBL and CP forests, (c) a patch tile contains NMX and NBL forests, (d) a patch tile contains NMX and NBL forests.