## 4 Analysis and Discussion  
### Accuracy Assessment  
The number of objects at specific steps is listed in Table 3, including segmentation, CVA, classification, training samples, and the final edited results. The result indicates that the potentially changed objects selected by the unsupervised CVA greatly narrow the region of interests. The supervised classification further removes false changes, accounting for nearly a half of those potentially changed objects. The percentage of manually delineated objects was from 15% to 35% of the total objects. Although we edited the final result to derive the accurate interference degree, the semi-automatic workflow was far more efficient than pure manual work. In practice, the segmentation result has already provided the boundary information; therefore, the missing objects can be copied from the segmentation result, without actually delineating their boundaries.  
To demonstrate the effectiveness of the proposed workflow, we validated the accuracy of the classification and change-detection results. For classification, we assessed the overall accuracy and the \(F\)-score [45] for each class based on all the classified objects. For change detection, three popular measures were used, including the \(F\)-score, precision, and recall (Equations (4)-(7)). The accuracy of change detection was based on the whole study site, where the true changes were interpreted visually from VHR images, assisted by field visits.  
\[F\text{-score}=\frac{2\cdot\text{precision}\cdot\text{recall}}{\text{ precision}+\text{recall}}, \tag{4}\]  
\[\text{overall accuracy}=\frac{TP+TN}{TP+FP+TN+FN}, \tag{5}\]  
\[\text{precision}=\frac{TP}{TP+FP}, \tag{6}\]  
\[\text{recall}=\frac{TP}{TP+FN}, \tag{7}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) represent true positive, true negative, false positive, and false negative, respectively. The accuracy of classification and change-detection results at two processing levels of the developed workflow are shown in Figures 8 and 9, respectively.  
The classification accuracy suggests the overall performance of the RF classifier was between 0.85 to 0.93, which was generally better than the change-detection accuracy. Since we only used the changed/unchanged label in the classification results to refine the change-detection results, some misclassified objects at the individual image may still lead to a correct change-detection result. In general, the \(F\)-Score of the change-detection result was around 0.7 at Level I (i.e., unsupervised CVA method only) and improved to 0.85 at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Yungang Grototes** & \
\begin{tabular}{c} **Wooden Pagoda of** \\ **Yingxian** \\ \end{tabular} &
\begin{tabular}{c} **Chengcun Ancient** \\ **City Ruins** \\ \end{tabular} \\ \hline Segmentation & 15,877 & 19,995 & 7286 \\ CVA & 1942 & 135 & 647 \\ Training samples & 2015 & 145 & 48 & 82 \\ (bi-temporal) & 2020 & 162 & 46 & 78 \\ Classification & 1304 & 96 & 440 \\ Manual deletion of false changes & 185 & 19 & 118 \\ Manual delineation of missing changes & 147 & 9 & 16 \\ Final result & 1272 & 85 & 338 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of objects after each processing step.  
Level II (i.e., supervised classification). All three cases showed an increase in the precision, indicating that some false changes included in the unsupervised CVA result can be removed through supervised classification.