Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

This work addresses mechanical defocus in Earth observation images from the IMAGIN-e mission aboard the ISS, proposing a blind deblurring approach adapted to space-based edge computing constraints. Leveraging Sentinel-2 data, our method estimates the defocus kernel and trains a restoration model within a GAN framework, effectively operating without reference images. On Sentinel-2 images with synthetic degradation, SSIM improved by 72.47% and PSNR by 25.00%, confirming the model's ability to recover lost details when the original clean image is known. On IMAGIN-e, where no reference images exist, perceptual quality metrics indicate a substantial enhancement, with NIQE improving by 60.66% and BRISQUE by 48.38%, validating real-world onboard restoration. The approach is currently deployed aboard the IMAGIN-e mission, demonstrating its practical application in an operational space environment. By efficiently handling high-resolution images under edge computing constraints, the method enables applications such as water body segmentation and contour detection while maintaining processing viability despite resource limitations.

Autonomous Payload Thermal Control

In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of the electronics makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challenge, taking advantage of the advent of increasing intelligence on board satellites, a deep reinforcement learning based framework that uses Soft Actor-Critic algorithm is proposed for learning the thermal control policy onboard. The framework is evaluated both in a naive simulated environment and in a real space edge processing computer that will be shipped in the future IMAGIN-e mission and hosted in the ISS. The experiment results show that the proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges, complementing traditional thermal control systems.

Image-to-image Translation as a Unique Source of Knowledge

Image-to-image (I2I) translation is an established way of translating data from one domain to another but the usability of the translated images in the target domain when working with such dissimilar domains as the SAR/optical satellite imagery ones and how much of the origin domain is translated to the target domain is still not clear enough. This article address this by performing translations of labelled datasets from the optical domain to the SAR domain with different I2I algorithms from the state-of-the-art, learning from transferred features in the destination domain and evaluating later how much from the original dataset was transferred. Added to this, stacking is proposed as a way of combining the knowledge learned from the different I2I translations and evaluated against single models.