# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 3 Results
### Qualitative Assessment  
We have seen that the methodology is applied at the patch level. For the performance analysis, the patches obtained from the ground truth images were therefore divided into three categories: patches without changes, patches with more than 10% of pixels changed (i.e., out of 1024 total pixels, 102 are changed) and patches with less than 10% of pixels changed. The percentage of 10% was chosen because it represents the average extension of the changed areas within the patch. This allowed for analysis of the performance, either on patches with significant changes or on patches with smaller changes. Moreover, it should be specified that patches reporting less than 1% of pixels as changed (i.e., out of 1024 total pixels, 10 are changed) were considered no change patches. This choice allows for discarding of the patches with cropped changed areas from the OSCD ground truth and of the patches with irrelevant alterations from the CVA change ground truth. For each pair of patches, the ED values were normalized in the 0-1 interval and were compared with their respective ground truth labelling. In Figure 12, the graphs report, for the test image pairs shown in Figure 11 and for both the AANN and DWT approaches, the ED values on the y-axis, while the patch's identifying  
Figure 10: Scheme of the change detection. number is reported on the \(x\)-axis. Each point is then colored according to the corresponding OSCD ground truth: patches without changes are shown in blue, patches with more than 10% of pixels changed are in red and patches with less than 10% of pixels changed are in green. We see that for both methods, the PVRs associated with no change patches are mainly localized in the lower part of the graph, while the PVRs related to a high number of changed pixels are in the upper area. Few PVRs in the green class (changed pixels below 10%) report ED values near zero, but this never happens for examples belonging to the red class. The misclassified samples seem to grow with the DWT dimensionality reduction. The graphs also point out the different response of the two considered images. Test image number 2 (couple (d) in Figure 11) performed better with both the AANN and DWT, and a clearer distinction between blue and red markers can be observed. On the contrary, test image number 1 (couple (a) in Figure 11) shows some no change PVRs at high ED values. However, it has to be remembered that the OSCD ground truth does not consider natural changes, such as vegetation growth. In an ideal setting, the ED values related to a no change condition should be the lowest and grow as the number of changed pixels increases. This trend is evaluated in Figure 13, which reports the correlation between the number of changed pixels in a ground truth patch and the respective ED. As for Figure 12, the graphs are shown for the AANN and DWT algorithms. Indeed, the presence of a linear correlation can be observed, especially for the AANN method. It also has to be noted that the linear trend is disturbed by the significant number of the no change patches, and this situation is enhanced in test image number 1. Figure 11: Examples 1 and 2 from test dataset: (**a1,d1**) images acquired at the time t1 in RGB composition; (**a2,d2**) images acquired at the time t2 in RGB composition; (**b,e**) OSCD ground truth; (**c,f**) CVA ground truth (changed areas are shown in white). The same analysis was repeated considering the ground reference obtained with the CVA method. The corresponding graphs are shown in Figures 14 and 15. Here, the EDs related to a condition of significant change (markers in red) are distinctly separated from the unchanged ones, especially in test image 2. Figure 15, compared to the previous case, shows a higher linear correlation between the number of changed pixels and the ED values. The performance is generally better with respect to the OSCD ground-truth.