## 4 Discussion  
Figure 4 is an example of how the algorithms used in this study segmented the PWDT. The first column is the test image for detection, the second is the ground truth image of the PWDT, and the third through sixth columns show the detection results of each algorithm. The five test images represent a variety of forest environments, including PWDT logged areas and bare ground.  
Following is a summary of the findings for each image. Image 1 in Figure 4 shows a heavily logged area, and all algorithms fail to find early PWDT that are beginning to brown. SegNet did not detect any shaded PWDT, and U-Net and DeepLabV3+ barely detect the location of PWDT rather than their shape. DeepLabV3+ (ResNet50) found most of the PWDT, but false positives occurred in broadleaf trees that had begun to brown. Image 2 in Figure 4 shows the detection of PWDT near logged trees. While most algorithms are able to detect damaged trees, they are unable to detect PWDT with very small canopy widths or in shaded areas. Only DeepLabV3+ (Res-Net50) was able to detect PWDT with atypical shapes near felled trees. Image 3 in Figure 4 shows the detection accuracy in a mixed forest. All but DeepLabV3+ (ResNet50) showed false positives for browned hardwoods. According to the National Forest Statistics, the area of mixed forests in Korea is 28.1% of the total forest area,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **IOU** & **Accuracy** & **Precision** & **Recall** & **f1-Score** \\ \hline U-Net & 0.563 & 0.997 & 0.776 & 0.673 & 0.720 \\ SegNet & 0.531 & 0.997 & 0.760 & 0.638 & 0.694 \\ DeepLab V3+ (ResNet18) & 0.536 & 0.996 & 0.725 & 0.672 & 0.698 \\ DeepLab V3+ (ResNet50) & 0.590 & 0.997 & 0.757 & 0.727 & 0.742 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of different algorithms.  
Figure 4: Segmentation results by the 4 algorithms for different land covers.  
and if there is an outbreak of PWD in mixed forests, utilizing the DeepLabV3+ (ResNet50) algorithm will provide the highest detection accuracy. Alternatively, in the case of mixed forests, the detection accuracy can be improved by utilizing images from May, after the new leaves have appeared. Image 4 in Figure 4 shows late-stage dead trees that were not cut down in time. None of the algorithms is able to detect PWDT that have long since dried up and turned gray (dead PWDT). Dead PWDT are trees that have not been logged and fumigated in the current year and are the main cause of the spread of the disease. Not all affected trees are controlled due to the budget and period of control, and these dead PWDT are relatively lacking in training data for analysis because there are not many of them; this likely resulted in the failure to detect them as PWDT. Image 5 in Figure 4 shows the results for an area containing bare ground and broadleaf trees with foliage. U-Net has the highest false positive rate, followed by DeepLabV3+ (Res-Net50), with most of the false positives coming from broadleaf trees with foliage that is affected by daylight.  
In detail, false positives occurred in roads, building rooftops, and daylight-affected broadleaf trees (Figure 5). Detection accuracy decreased for shaded PWDT or a grayish color PWDT that were long-dead and nearly leafless. In Images 2 and 4 of Figure 4, all but DeepLabV3+ (ResNet50) failed to detect PWDT that overlapped with surrounding pines and did not have a typical canopy shape. In Image 3 of Figure 4, all algorithms except DeepLabV3+ (ResNet50) detected the browned hardwoods as PWDT.  
Overall, DeepLabV3+ (ResNet50) showed the best detection performance among the algorithms. Segnet and U-Net are more useful in detecting the exact shape and boundary, which is more useful for analyzing the area affected by PWD. In the case of DeepLabV3, the location accuracy of damaged trees is high, so it would be more desirable to quickly confirm suspected trees through field trips.