# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 3 Experiment and Analysis
### Comparison Experiments  
In this paper, we select the mainstream DD-CNN [67], Trans Unet [68], and ESOFCM [69] in change detection to verify the performance of the HF-CRF model proposed in this paper on the Berne, the Ottawa, the Mexico, and the San Francisco datasets; the change-detection results are shown in Figures 15-18, respectively, and the performance index analysis results are shown in Tables 5-8. Figure 17: Detection results of different methods on Mexico data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **FP** & **FN** & **OE** & **PCC** & **Kappa** \\ \hline DD-CNN & 130 & 200 & 330 & 99.64\% & 0.8596 \\ TransUnet & 350 & 49 & 399 & 99.39\% & 0.7925 \\ ESOFCM & 112 & 199 & 311 & 99.65\% & 0.8643 \\ Our Method & 105 & 187 & 292 & 99.68\% & 0.8673 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of different methods on the Berne dataset. Figure 18: Detection results of different methods on San Francisco data. The green boxes indicate the parts where the performance of comparison methods that have poor performance, including the blurred boundary and the false alarm phenomenon. The detection results of the four methods on the Berne dataset are shown in Figure 15b-e. The comparison of the red areas shows that the TransUnet method has the blurred contours and higher false alarms, followed by DD-CNN, while ESMOFCM has obviously missed alarms, and the proposed method maintains clearer and more accurate segmentation boundaries, with more complete contours. The performance on the Ottawa dataset is shown in Figure 16b-e. As shown in the green box area 1 of Figure 16, the TransUnet model forms larger joint holes in the hole area with serious leakage, the ESMOFCM model produces more holes and leakage in the green area 2, the DD-CNN and HF-CRF models have more complete contours overall with less leakage, while the contours of HF-CRF model are clearer than the DD-CNN model. From the results in Table 6, the overall results of the ESMOFCM model and TransUnet are not satisfactory; the DD-CNN model has more balanced false and leaky alarms and higher Kappa coefficients, and the HF-CRF model has slightly higher false alarms and low leaky alarms but the lowest total errors, so it achieves the highest detection performance, which is due to the better segmentation baseline achieved by the D-DRUNet network. The dynamic second-order potential function further refines the contours and edges by iteration, but the balance of the HF-CRF false alarms is not enough, and the number of missing detections is higher. The next improvement step of the proposed method in this paper will also focus on reducing the missed alarms. Figure 17 shows the detection results of different models on the Mexico dataset. The ESMOFCM model has more false alarms and a poor detection performance, due to background interference. In contrast, the DD-CNN, TransUnet, and HF-CRF models have more complete contours. However, the DD-CNN and TransUnet models have blurred boundary information and missed alarms in certain areas. On the other hand, the proposed method captures more detailed information and has a clear-edge detection effect. According to Table 7, the proposed method achieves optimal performance in terms of both PCC and Kappa evaluation indexes by reducing false detections and missed alarms. The detection results of the methods in this paper on the San Francisco data are presented in Figure 18. All four methods obtain more complete change-region detection results. Both modal difference images of the San Francisco dataset are strongly disturbed by the background when modal complementarity is weak, so the performance advantage of HF-CRF for change-detection results on this dataset is diminished, while the FCM structure