A Misalignments Correction Method for SDGSAT-1 Glimmer Imagery Based on Object Detection and Cross Correlation

As the first satellite specifically designed to serve sustainable development goals (SDGs), the Sustainable Development Science Satellite 1 (SDGSAT-1) has attracted considerable attention by providing panchromatic glimmer imagery with an unprecedented clarity of 10 m. However, the unsatisfactory positional accuracy of glimmer imagery hinders further fine-grained studies. This study identifies the cause of these positional errors as misalignments generated during the data production process and thus proposes a correction method based on deep learning object detection and cross correlation. The method achieves accurate measurements of the position, direction, and distance of the misalignment, which are used to correct the glimmer imagery and to effectively improve the positional accuracy. In this study, we: 1) propose a simulated misalignment dataset that demonstrated with excellent performance during the training of multiple deep learning object detection models, reaching up to 99.44% average precision (AP); 2) propose pixel brightness correlation coefficient based on cross correlation and validate its effectiveness to characterize misalignments; 3) improve detection efficiency by the threshold design of window average brightness and object detection confidence; and 4) explore the reasons for the misalignment suggests that it is the difference between the satellite’s designed operating time and the actual operating time that leads to the charge-coupled device (CCD) stitching flaw. Our proposed method can contribute to evaluating and improving the positional accuracy of SDGSAT-1 panchromatic glimmer imagery, providing a way to obtain datasets with good spatial consistency and to conduct related research.