Applications and Enabling Technologies for On-Board Processing and Information Extraction: Trends and Needs

The growing interest in on-board data processing for Earth observation satellites has been driven by both the needs of ground-based applications of satellite data and the increasing challenges borne by the latest on-board instrument technologies. On the application side, end users of satellite data are seeking methods of acquiring more timely data and lower latencies, supplied in a form that is immediately useful to them. On the technology side, instrument manufacturers are developing payloads with higher spatial and spectral resolutions and greatly increased acquisition rates. Such rates far exceed typical current and near-term downlink bandwidths, resulting in severe data bottlenecks. Addressing the application needs leads to solutions which also address the challenges of adopting these new instruments. On-board processing activities which can extract information from data, reduce the data and prioritise useful information can not only deliver data to end users faster and in a more useful form, they can also filter and intelligently compress data to reduce or eliminate the downlink bottleneck. These on-board activities are many and variable, depending on the on-board functionality desired and the end user requirements to meet. In this paper, generic on-board applications are presented which may be configured to target groupings of use cases and provide measurable end benefits to mission stakeholders. These use cases have been solicited from a comprehensive survey and a workshop and one-to-one engagements with remote sensing end users. These applications are underpinned by enabling technologies which leverage the state-of-the-art in embedded processing and AI. These technologies include machine learning algorithms (both traditional and deep learning), training datasets, processing architectures and embedded computing devices. The paper summarises the algorithms which can be used to implement the on-board applications and the datasets which can be used to train and test these algorithms. Finally, a summary of the requirements and further implications of adopting these technologies to implement the proposed applications is presented. This leads to several impact areas and corresponding needs that must be addressed in the design of future on-board processing systems