Adaptive and Robust Image Processing on CubeSats

TL;DR

This paper presents DIPP and DISH, two systems that enhance adaptability, robustness, and efficiency of image processing on resource-limited CubeSats, enabling flexible and reliable Earth observation missions.

Contribution

Introduction of DIPP and DISH, novel systems that improve flexibility, robustness, and resource efficiency of image processing pipelines on CubeSats.

Findings

DIPP reduces network updates and errors with negligible overhead.

DISH offers comparable expressiveness to Lua with lower memory use.

Experiments confirm DIPP's robustness and DISH's efficiency.

Abstract

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.