# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 4 Discussion  
The method proposed here aims to develop a change detection technique with a compressed representation of the two considered images. The smaller representation can be obtained by means of an AANN or DWT, but PVRs extracted with an AANN led to better results in change detection.  
The results were validated by means of OSCD ground truth, but, as highlighted in Section 2.1, these images report only man-made changes, leaving out the natural changes. This characteristic negatively impacts the results, moreso for test images showing a natural environment than those with an urban setting. Therefore, validation of images obtained by means of CVA were also considered. As described in Section 2.2, this method depends on the analyst's sensitivity but was demonstrated to be effective in this case, overcoming the critical issues related to OSCD ground truth.  
Compared to the study described in [25], our work proposed a method focused on detecting any type of change and was tested on Sentinel-2 images that were easy to access and use. Additionally, the detection was performed on a compressed representation of the original acquisition. In [16], [PERSON] et al. used NNs and Spot-5 images at 10 m of spatial resolution, although the algorithm can be applied to any type of satellite acquisitions. The changed areas were highlighted through an image pixel-based subtraction. In this case, compression of the image is not performed to reduce the amount of data but to apply the subtraction method. Indeed, subtraction works fine on lower resolution images, but the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**AUC**} \\ \hline
**Ground Truth** & **AANN** & **DWT** & **AANN** & **DWT** & **AANN** & **DWT** & **AANN** & **DWT** \\ \hline OSCD & 0.63 & 0.58 & 0.58 & 0.3 & 0.60 & 0.39 & 0.909 & 0.827 \\ \hline CVA & 0.78 & 0.71 & 0.97 & 0.52 & 0.86 & 0.60 & 0.997 & 0.952 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for test image 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**AUC**} \\ \hline
**Ground Truth** & **AANN** & **DWT** & **AANN** & **DWT** & **AANN** & **DWT** & **AANN** & **DWT** \\ \hline OSCD & 0.12 & 0.08 & 0.31 & 0.14 & 0.17 & 0.10 & 0.607 & 0.514 \\ \hline CVA & 0.87 & 0.82 & 0.99 & 0.99 & 0.92 & 0.90 & 0.977 & 0.965 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for test image 3.  
pixelwise analysis may not be compliant with on-board processing requirements. [PERSON] et al. [17] used multi-temporal hyperspectral images as the input dataset and two NN models. The features extracted by the first network are considered additional input for the second NN, which performs the change detection. They achieved more than 95% accuracy, but the complexity of the scheme does not seem developed to be run on-board.  
The testing phase led to promising results, as shown in the figures in Section 3.1, although it revealed the criticality related to the definition of a unique threshold to automatically perform the change detection. In fact, this value strictly depends on the scene depicted in the image. For this reason, numerical results were evaluated for two images of the test set that are similar in terms of land cover. By validating with the CVA, the AANN reached higher score values than the OSCD ground truth, and the gap with the DWT algorithm is sharply marked for test image 3. Generally, the results obtained by validating with the CVA were significantly better than the ones obtained with the OSCD.