# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods
### Description of the Reference Data  
The OSCD ground truth images were generated manually by comparing the true colour composition of each image pair. However, as explained in Section 2.1, in this case natural changes (e.g., vegetation growth or sea tides) are not considered [31]. For this reason, another level of reference truth was generated for evaluation of the results. This has been obtained by applying a change vector analysis (CVA) to the original pair of images. CVA is a widely used change detection technique for optical images because it can produce detailed information and can process any number of input bands. In the procedure, the pixelwise difference in spectral measurements is computed and all difference values greater than a predefined threshold are labelled as changed pixels [13; 24].  
In CVA, the threshold determination is a critical task, and, to this end, an empirical method based on the direct observation of the test image pairs, already proposed in [32], has been considered. In more detail, the five labelled OSCD image pairs belonging to the test  
Figure 3: Sample from the OSCD dataset: (**a**) image acquired at time t\({}_{1}\) (RGB); (**b**) image acquired at time t\({}_{2}\) (RGB); (**c**) ground truth (changed areas are shown in white).  
Figure 2: Samples from the Sentinel-2 dataset, 10 m spatial resolution (RGB).  
set were considered first. For each image pair, all areas showing significant changes were extracted. Here, we included both artificial changes (already made available by the OSCD dataset) and natural changes that have been detected by additional CVA. An example of an additional changed area is shown within the yellow polygon in Figure 4. For each changed area, the average pixel value at time t\({}_{1}\) and at time t\({}_{2}\) and the difference between such average values were computed. The threshold was then determined in order to minimize the number of false positives and false negatives obtained on the described set of examples. The procedure was repeated for the three bands: R, G and B. It was found that, ignoring small alterations, the selected thresholds were sensitive enough to highlight the relevant changes. Finally, the determined thresholds were applied on the entire pixelwise difference images to generate the reference binary maps.  
The CVA depends on the analyst's identification of suitable factors; however, it can offer a reliable benchmark comparison, and a meaningful example is provided by the test image reported in Figure 3. The difference between the OSCD and CVA ground truth images are shown in Figure 5: the large white areas in Figure 5 highlight the modifications in land cover that are not reported in Figure 5a. Indeed, the ground truth image obtained by means of CVA was specifically made to point out these alterations.  
Figure 4: Example of an area used for the threshold determination: (**a**) the average value of the changed pixels in the yellow polygon is 1700 at time t\({}_{1}\) and (**b**) 740 at time t\({}_{2}\). (**c**) Binary image obtained with 1000 as threshold value (changed areas are shown in white).  
Figure 5: Comparison between (**a**) OSCD and (**b**) CVA ground truth images for test image shown in Figure 3 (change areas are shown in white).