Enhancing Satellite Image Coregistration Using Mirror Array as Artificial Point Source for Multisource Image Harmonization

Precise co-registration of multi-sensor, multispectral and multi-temporal satellite imagery is crucial for effective remote sensing applications. This paper presents an advanced method for satellite image co-registration using mirror arrays as artificial point sources, developed at the Center for Research and Application of Satellite Remote Sensing of Yamaguchi University (YUCARS), Japan. We combined highreflectivity acrylic mirror arrays with Fourier-based phase correlation techniques (AROSICS) for sub-pixel shift estimation to achieve accurate inter-sensor and band-to-band registration. The study focused on co-registering GRUS-1 (Axelspace Corporation) satellite imagery with Sentinel-2 data and improving band-to-band co-registration within GRUS-1 imagery, as well as finding the optimal combination of global (single tie point) and local (tie point grid) co-registration methods. A comparison of commonly used resampling techniques – nearest neighbor, bilinear interpolation, and cubic convolution – was also performed to assess the tradeoffs between image quality and positional accuracy. Results show notable improvements in geolocation accuracy, with RMSE values of 3.59, 4.05, and 4.14 meters in the sub-pixel range for three different GRUS-1 bands when compared to the Global Reference Image (GRI). The mirror array proved particularly effective in combination with the local method (global to local), and demonstrated superior results in high displacement cases on its own (global only), offering a reliable alternative to traditional co-registration methods with less loss of information due to resampling. Furthermore, this study builds upon previous research demonstrating the potential of mirror arrays for both geometric and radiometric calibration, highlighting the versatility of this approach in enhancing overall satellite image quality and consistency.