Advanced Super-Resolution Techniques for Optical Payloads in Earth Observation: Combining Traditional and Deep Learning Methods

Small satellite platforms are increasingly used for Earth observation due to their cost-effectiveness and flexibility. However, their limited payload size often results in reduced spatial resolution of captured images. In our work, we address this challenge by proposing an advanced multi-image super-resolution (MISR) approach tailored for small satellite applications.

It integrates:

• Sub-pixel image registration and on curvelet transform-based interpolation to preserve high-frequency details while reducing artifacts;
• A novel hybrid method called SP-MISR (Subpixel Multi-Image Super-Resolution), which leverages Convolutional Neural Networks (CNNs) for local detail analysis and Transformers for global spatial relationships.

Our experimental results demonstrate that this combined approach  significantly improves image sharpness, preserves fine details, and reduces artifacts, outperforming traditional super-resolution techniques. Moreover, SP-MISR exhibits robustness in processing noisy and distorted images, making it particularly suitable for the constrained imaging systems of small satellites.

Future developments will focus on improving computational efficiency, reducing interpolation errors, and extending the method to multi-spectral imaging and interplanetary missions, by exploring explore pure deep learning techniques.

This work highlights the potential of integrating traditional and deep learning methodologies to enhance image quality, thus expanding the scientific and operational capabilities of small satellite missions.