High-performance on-board data processing is a strategic technology for space agencies and other space organizations. Here are a few reasons why it is crucial:

  1. Data Volume Reduction: Space missions generate vast amounts of data, often exceeding the available downlink bandwidth. On-board data processing allows for real-time analysis and filtering of data, reducing the volume that needs to be transmitted to the ground. This enables more efficient use of limited communication resources and facilitates faster decision-making.

  2. Latency Reduction: The distance between Earth and spacecraft can cause significant communication delays. By processing data on board, time-critical operations can be executed without waiting for commands from the ground. This is particularly important in scenarios such as autonomous navigation, hazard detection, and response to critical events.

  3. Autonomy and Fault Tolerance: On-board data processing enables spacecraft to operate autonomously, making independent decisions based on processed data. This autonomy is crucial in scenarios where real-time communication with the ground is not possible or in situations where rapid responses are required. Additionally, on-board processing allows for fault detection and mitigation, enhancing the reliability and safety of space missions.

  4. Science and Exploration: High-performance on-board data processing enables scientific instruments to analyze data in real-time, allowing researchers to rapidly identify interesting phenomena and optimize data collection strategies. This capability is particularly valuable for missions involving remote sensing, planetary exploration, and astronomy.

Today’s satellites are inherently inflexible and purpose-built for a single mission, relying heavily on the ground segment to analyze data. The space market is looking for on-board flexibility / on-board reconfiguration targeting the digitalization of payload for earth observation and space exploration missions. Furthermore, there is an increasing interest for the availability of AI-oriented hardware devices allowing tasks such as deep learning inference and pre-processing of sensor data in-orbit.

OHB is constantly exploring disruptive approaches to propose its customers always better space-based solutions. From its existing heritage in the domain, the company is exploring the possibility to transfer part of the processing of satellite data from the earth (ground segment) to space (space segment), through the development of so-called On-board Payload Data Processing (OPDP).

Some example of applications:

  • Data fusion on board
  • Automatic optical sensor calibration
  • End-to-End secured data transfer
  • EO data delivered with low latency. Filtering onboard the data of interest increasing the flexibility in usage of bandwidth, data storage and coverage.
  • Autonomy for space exploration missions, processing done with speed and efficiency without intervention from Earth.

The technology can be an enabler for new applications, for example:

  • In-orbit distributed calculation
  • Fleet cooperation
  • Automation of mission control and operation
  • Satellite as a service


Satellite as a Service


We are currently examining the possible combinations of software framework and hardware from the use of AI accelerator to high-performance mixed-criticality avionics electronics. Our work includes design mitigation techniques to include this development in existing OHB satellite platforms.

Based on the existing group heritage and competences concerning onboard data processing, OHB Hellas is strongly committed to providing a turn-key system with a combination of AI-oriented hardware and open source software to bring intelligence on board while providing a high level of flexibility. This solution would enhance the group portfolio and further improve its competitiveness for future earth observation and space exploration missions.

National Defense and Civil Security from Space

Through high-performance on-board data processing, the response time to critical events can be reduced, as data is available to decision makers faster. The sensitive geographic positioning of Greece creates a need for national defense applications from space. Applications of civil security are critical for the country as well, given the catastrophic phenomena that occur frequently, such as wildfires and floods. High performance on board data processing, coupled with technologies such as virtualization and flexible ground stations can provide innovative platforms that are part of a distributed network. This network can benefit remote sensing applications from space which can be implemented to answer Greece's needs from space and create a sovereign product at the same time, as the heritage already exists in the country.

Onboard Multi-Frame Super Resolution Image study

Super-resolution refers to the process of improving the spatial resolution, that is, the level of detail, of an image or an acquisition system. Our innovative idea is to use OPS-SAT's powerful on-board computer to deploy a machine learning-based super-resolution algorithm directly on-board the satellite. It has the potential of opening new perspectives of EO applications mainly in time-critical domains like safety and security. In the frame of this experiment, we successfully developed and deployed an AI-based multi-frame super resolution algorithm on board OPS-SAT achieving a notable improvement in image quality and proving the merits of the proposed approach. The study was undertook by OHB Hellas with FORTH as a subcontractor.

Cognitive Cloud Dual Camera study

The main objective of this study, undertook by OHB Hellas with FORTH as a subcontractor, is the evaluation of the merits of a dual camera setup for the acquisition of both large swath, low- resolution images and narrow-swath, high-resolution images of identified events of interest, on-board, through AI methods. The feasibility of the approach on a mission-level concept of a single-satellite, dual camera setup as well as a two-satellite (leading-trailing), single camera setup was investigated. The AI approach was demonstrated on commercially available representative high-performance on-board computers (Xilinx Kria SoM / Google Edge TPU SoM) through a state-of-the-art fire detection use case. 


The goal of this project is to evaluate the suitability of COTS hardware accelerators (Google Edge TPU) for use in space missions, focusing on radiation characterisation, performance evaluation and software support allowing efficient uploading of new AI/ML models to the satellite during flight. It was developed with National Technical University of Athens as prime contractor.