Satellite On-Board Change Detection via Auto-Associative Neural Networks

The increase in remote sensing satellite imagery with high spatial and temporal resolutions has enabled the development of a wide variety of applications for Earth observation and monitoring. At the same time, it requires new techniques that are able to manage the amount of data stored and transmitted to the ground. Advanced techniques for on-board data processing answer this problem, offering the possibility to select only the data of interest for a specific application or to extract specific information from data. However, the computational resources that exist on-board are limited compared to the ground segment availability. Alternatively, in applications such as change detection, only images containing changes are useful and worth being stored and sent to the ground. In this paper, we propose a change detection scheme that could be run on-board. It relies on a feature-based representation of the acquired images which is obtained by means of an auto-associative neural network (AANN). Once the AANN is trained, the dissimilarity between two images is evaluated in terms of the extracted features. This information can be subsequently turned into a change detection result. This study, which presents one of the first techniques for on-board change detection, yielded encouraging results on a set of Sentinel-2 images, even in light of comparison with a benchmark technique.