TLDR: This research introduces two learning-based planning methods, reinforcement learning (Q-learning) and imitation learning (behavioral cloning), to enhance the scientific data collection of Earth observation satellites. These methods, which build on dynamic programming concepts, intelligently reconfigure and point satellite instruments using lookahead data. Evaluated against existing heuristic methods in cloud avoidance and storm hunting scenarios, the learning approaches significantly outperform them, with reinforcement learning achieving 13.7% better performance and imitation learning 10.0% better than the best heuristic. The study also shows that these methods can be effectively trained with relatively small datasets, demonstrating a promising path for more autonomous and efficient satellite operations.
Earth-observing satellites are invaluable for gathering scientific information about our planet, aiding fields like geology, meteorology, and climate science. However, these sophisticated instruments face inherent limitations. They are largely confined to their orbital paths, their sensors have a restricted field of view, and operating and pointing these sensors consume a significant portion of the spacecraft’s resources. This makes it crucial for satellites to optimize the data they collect, focusing only on the most important or informative measurements.
A concept known as dynamic targeting is emerging to address these challenges. It involves using satellite resources and data from a ‘lookahead’ instrument to intelligently reconfigure and point a primary instrument. Previous studies have indicated that dynamic targeting can significantly increase the amount of scientific information gathered compared to traditional, less intelligent sampling strategies.
This research introduces two novel learning-based approaches to dynamic targeting: reinforcement learning (specifically Q-learning) and imitation learning (through behavioral cloning). These methods build upon a dynamic programming solution, which helps plan optimal sequences of sampling locations. The goal is to determine the most efficient way for Earth observation satellites to sample their surroundings while adhering to practical constraints, particularly power consumption.
The satellite model used in this work features a primary radar sensor and a lookahead sensor. The lookahead sensor provides a limited view of the satellite’s future path, allowing for proactive decision-making. This setup is similar to missions like the Smart Ice Hunting Radar (SMICES), which uses a radiometer to find storms along the satellite’s path for its radar to target.
The study evaluated these new learning approaches against existing heuristic methods for dynamic targeting, such as random, greedy nadir, greedy lateral, greedy radar, and greedy window sampling. These heuristics represent different levels of environmental knowledge and sampling capabilities. The dynamic programming approach served as an ‘oracle,’ providing an upper bound on performance, as it assumes full knowledge of all future states, which is not practical for real-world satellite deployment.
Experiments were conducted in two scenarios: cloud avoidance and storm hunting. For cloud avoidance, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used, classifying pixels into clear (high scientific reward), mid-cloud (medium reward), and cloud (low reward). For storm hunting, data from the Global Precipitation Measurement (GPM) mission classified storm types into no storm, rainy anvil, and convective core, each with varying scientific rewards.
The results demonstrated significant improvements. Imitation learning performed, on average, 10.0% better than the best heuristic method (greedy window), while reinforcement learning performed even better, achieving an average of 13.7% improvement. Both learning methods also showed that they could be trained effectively with relatively small amounts of data, making them practical for satellite applications where computational resources and data availability might be limited.
In the cloud avoidance scenario, Q-learning and behavioral cloning achieved 98.67% and 95.87% of the possible reward on average, respectively, compared to the greedy window method’s 87.50%. For storm hunting, Q-learning and behavioral cloning attained 94.66% and 91.27% of the possible reward, while the greedy window method reached 82.57%. These findings highlight the superior ability of learning-based methods to utilize lookahead sensor data for more scientifically rewarding samples.
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This research paves the way for more autonomous and efficient Earth observation missions, ensuring that satellites collect the most valuable scientific data possible. Future work aims to incorporate more realistic satellite and instrument constraints, explore more complex reward models, and use full images as inputs to the learning algorithms instead of manually engineered state vectors. The ultimate goal is to deploy and test these algorithms on actual satellite platforms. You can read the full research paper here.
