A fast pansharpening method based on object-space-consistency and GPU parallel processing

The fusion of panchromatic and multispectral images is a prerequisite for obtaining multispectral images with high spatial resolution (HRMS). The traditional data processing mode requires that the acquired panchromatic data and multispectral data be transmitted to the ground through the star-ground link, and then a series of correction, registration and fusion operations are conducted in order to obtain HRMS. This processing mode has low timeliness and cannot meet the demand of on-board real-time processing. To solve this problem, we perform efficient registration and fusion processing for the images containing the target region acquired in real time during satellite orbit. Specifically, we propose a fast virtualized registration method based on object-space-consistency, which is capable of obtaining accurately registered images under the premise of ensuring real-time performance. On this basis, we utilize GPU acceleration to parallelize the time-consuming steps of the traditional fusion algorithm, thereby achieving rapid fusion processing while guaranteeing the fusion performance. Through experiments on an on-board simulation system, our method has been able to achieve 15.10 times speedup compared to the traditional registration and fusion processing while guaranteeing good performance.