Adaptive and Robust Image Processing on CubeSats
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arXiv
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| Format: | Preprint |
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2025
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| author | Bayer, Robert Priest, Julian Kjellberg, Daniel Lindhard, Jeppe Sørenesen, Nikolaj Valsted, Nicolaj Óli, Ívar Tözün, Pınar |
| author_facet | Bayer, Robert Priest, Julian Kjellberg, Daniel Lindhard, Jeppe Sørenesen, Nikolaj Valsted, Nicolaj Óli, Ívar Tözün, Pınar |
| contents | CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors.
Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03152 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive and Robust Image Processing on CubeSats Bayer, Robert Priest, Julian Kjellberg, Daniel Lindhard, Jeppe Sørenesen, Nikolaj Valsted, Nicolaj Óli, Ívar Tözün, Pınar Image and Video Processing Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Machine Learning CubeSats offer a low-cost platform for space research, particularly for Earth observation. However, their resource-constrained nature and being in space, challenge the flexibility and complexity of the deployed image processing pipelines and their orchestration. This paper introduces two novel systems, DIPP and DISH, to address these challenges. DIPP is a modular and configurable image processing pipeline framework that allows for adaptability to changing mission goals even after deployment, while preserving robustness. DISH is a domain-specific language (DSL) and runtime system designed to schedule complex imaging workloads on low-power and memory-constrained processors. Our experiments demonstrate that DIPP's decomposition of the processing pipelines adds negligible overhead, while significantly reducing the network requirements of updating pipelines and being robust against erroneous module uploads. Furthermore, we compare DISH to Lua, a general purpose scripting language, and demonstrate its comparable expressiveness and lower memory requirement. |
| title | Adaptive and Robust Image Processing on CubeSats |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2506.03152 |