# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 5 Conclusions  
In a scenario where the demand for on-board processing to be run on space payloads is increasing more and more to limit both bandwidth and data storage, this work proposes a new method for on-board change detection based on NN. In particular, an AANN extracts the image features, which are then used for a CVA. AANNs are characterized by a very light architecture; hence, they are particularly suitable for on-board operations.  
In this study, the change detection algorithm was applied to Sentinel-2 images. Moreover, for performance analysis purposes, a standard image compressor based on DWT was also considered as a benchmark. The algorithms perform dimensionality reduction in multi-temporal input image patches by reducing their initial size into smaller representative vectors. A dissimilarity measure, i.e., the ED, was computed to indicate the probabilities of change.  
The main contributions of this work can be summarized in three aspects: First, it shows the capability of AANNs in image compression and reconstruction. As detailed in Section 2.5, the AANN achieved better results in reconstructing images than other well-known techniques, such as the DWT. Second, while most of the current change detection approaches use deep learning schemes [38], our algorithm provides an innovative shallow neural network technique using compressed representation. Lastly, the procedure presented here was designed and developed to be run on-board, and the on-board processing is currently another relevant topic. The computing resources and the memory required to store the training weights are comparable to those typically available on-board, as described in Section 1. The results demonstrated that for change detection purposes, image encoding by means of AANNs led to better achievements than the DWT and required low-level computing resources in the test phase, which could then be executed on-board. The results improved up to 0.2 in terms of the F1 score. The classification accuracy between the classes change and no change is strictly affected by the input dataset and the validation images. Indeed, datasets for change detection analysis are typically characterized by class imbalance and limited availability of ground truth images. Despite this, a promising distinction between EDs related to areas reporting significant changes and those for no change areas was shown, reaching an F1 score of 0.9.  
Further research will investigate the tailoring of the method for SAR data, since they allow the implementation of the algorithm in any weather condition and during the night.  
Conceptualization, G.G.; methodology, G.G. and F.D.F.; software, G.G.; validation, G.G., F.D.F. and G.S.; formal analysis, G.G., F.D.F. and G.S.; investigation, G.G.; resources, G.G., F.D.F. and G.S.; data curation, G.G.; writing--original draft preparation, G.G. and F.D.F.; writing--review and editing, F.D.F. and G.S.; visualization, F.D.F.; supervision, F.D.F. and G.S.; project administration, F.D.F. and G.S.; funding acquisition, F.D.F. and G.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.