## 3 Results and Discussion
### Study Area and Background
#### 3.1.2 Change Detection Based on the 2015 and 2016 Images  
The highlighted box labeled as Region 2 (Yanxi Lake) is used to give a detailed assessment. This region contains grassland, city, and water bodies, and the size is 800 \(\times\) 500 pixels. The main changes occurred in the water areas and the city. The RGB images in Pauli basis (\(|S_{hh}-S_{vv}|\) for red, \(|S_{hv}|\) for green, and \(|S_{hh}+S_{vv}|\) for blue) and the ground reference of the changed areas are shown in Figure 11.  
The results of the CI maps and their 3-D plots are shown by linear mapping to [0, 1] in Figure 12. Although the CI map of the log-ratio (HH) method (Figure 12a) shows a strong response in the changed area, the contrast between the changed and unchanged areas is not obvious. Moreover, there is also a high response in the unchanged area because of the speckle noise, which impacts the accuracy of the change detection map. The CI map of the CVA method (Figure 12b) shows a weak response, which makes it difficult to distinguish the changed and unchanged areas. The proposed method (Figure 12c) using the fully polarimetric information also shows a strong response in the changed areas. However, unlike the other methods, the CI values are almost homogeneous in the water bodies and forest areas. Overall, the proposed method of generating the CI map is effective at accurately distinguishing the changed and unchanged areas. After generating the CI maps, the change detection maps were obtained by the threshold methods of 5% significance level, 1% significance level, K&I, CFAR (Pfa = 0.005), and the proposed method. The results are shown in Figure 13, where it can be seen that the obviously changed areas can be easily detected by all the methods. In order to further verify the efficiency of the proposed method in generating the CI map, we used the same threshold strategy (improved K&I) in the methods of log-ratio of single-channel SAR, CVA, and test statistics, as shown in Figure 13a-d,i. As a result of the speckle noise and the CI map with lower contrast, the result of log-ratio and improved K&I based on single-channel bi-temporal SAR images contains many wrong detections in some unchanged areas. The other change detection method of CVA and improved K&I results in many missed detections. Due to the high quality of the CI map obtained using the fully polarimetric information based on the test statistic, the change detection map of the test statistic and improved K&I shows a better performance. These experiments show that using the multi-temporal full polarimetric data can achieve a better change detection result than using the single-channel or multi-channel data. In order to compare the effect of the threshold choice, we used the CI map based on the test statistic and the threshold methods of 5% significance level, 1% significance level, K&I, CFAR (Pfa = 0.005), and improved K&I. Among these methods of choosing the threshold, the 5% and 1% significance levels and K&I result in more false alarms in the city and forest than the proposed method. In addition, CFAR results in more false alarms in the water bodies than the proposed method. The quantitative comparison of these detection schemes shown in Table 3 also indicates that the proposed approach shows a better performance. The proposed approach of the test statistic and improved K&I achieves the best results  
Figure 11: RADARSAT-2 PolSAR images of Region 2 indicated in Figure 3, acquired on (**a**) 25 June 2015 and (**b**) 6 July 2016; (**c**) The ground reference map (white denotes change and black denotes non-change). in all four indicators (FA (%), TE (%), OA (%), and Kappa). This confirms that the proposed method is effective and shows a significant improvement over the other methods. The image of Yandong Lake (400 \(\times\) 400 pixels) contains grassland, city, and water bodies. The main changes occurred in the water areas, because of the rain.