Speaker
Description
Modern imaging sensors create frames of several megapixels at a high frame acquisition rate. These imaging sensors are placed onboard spaceborne platforms and can greatly enhance their capabilities. However, an industry trend towards smaller satellites - with smaller antennas, less power and worse pointing accuracy- leads also to an expectation that the downlink capability will remain well below the data generation capability for such imaging satellites. In order to use more acquisitions and have a high ‘usability’ of the satellite, onboard processing of payload data is a possible solution. With the rise of smartphones and tablets, the world of onboard processing has at its capacity a multitude of powerful and energy-efficient computing platforms available on the market today.
The FONDA (Flexible ONboard Data Analysis) project will determine and test the technology platform that is best suited for onboard intelligent processing of imaging payload data. Within the project, requirements will be collected from leading industry partners pushing boundaries on payload capabilities, such as SSTL and Honeywell. A range of suitable technology platforms for onboard processing will be identified and evaluated across the industry and market requirements, ultimately leading to a set of recommendations for technologies optimally suited for high capacity image processing onboard small spaceborne platforms.
During the FONDA project, the use of deep learning-based image processing is investigated. This is a more recent approach to image processing that has shown good results in many imaging inverse problems (e.g. denoising, super-resolution, deconvolution). The use of deep learning requires large amounts of training data and large computational resources to learn how to produce the desired outcomes from the examples. This learning can be done offline, and the resulting model is relatively lightweight and can be used onboard for the image processing. We use our latest experience from deep convolutional neural networks for interpreting large multispectral datasets within the Copernicus program, to assess their suitability and usefulness for implementation onboard smaller spaceborne platforms.
The current state of the art for onboard processing on imaging satellites is to reduce the resolution and create thumbnails which can be used for determining interesting images to downlink. The FONDA project explores the technological capabilities for creating informative data products that could then be downlinked instead of raw images, thus maximizing useful output per downloaded data unit. This would widen the applicability of the microsatellite technology including areas such as early warning and rapid response systems.
Even if it should be possible to utilize intelligent onboard payload processing for creating the desired outputs, another aspect of the investigation concerns the concrete algorithms and implementation choices and their resulting impact on how performant such intelligent processing can be. This would provide additional insights into the usability of intelligent onboard imaging satellite systems and the desired operational output.
| Paper submission | No |
|---|
