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On-board real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform statistical analysis or transformation on the entire image first, and then perform subsequent processing. There are problems such as high algorithm complexity and resource occupation, and it is difficult to apply to on-board scenarios where the volume and power consumption are strictly limited. Aiming at the requirements of on-board fusion, a real-time processing approach for high-resolution optical satellites is proposed. First, through the implementation of real-time geometric positioning, ROI extraction is completed while the camera is imaging, avoiding the disadvantages of traditional methods for processing large amounts of data; then, based on the principle of object-space consistency, by fine-tuning virtual sensor parameters, the registration of panchromatic multispectral image is completed in the sensor correction step, so that the relative accuracy of the two can meet the fusion requirements, and time-consuming pixel-level registration processing is avoided; finally, according to the characteristics of the algorithms and embedded hardware, an efficient algorithm mapping strategy is formulated, and deep optimization is implemented to achieve a significant improvement in performance. Experiments show that the performance of this method is improved by 156.23 times compared with the traditional method. Moreover, after building a parallel pipeline, it can meet the real-time fusion processing requirement of completing a 5000&#x002A;5000 pixels ROI area every 2.4 seconds.

Task-driven on-Board Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite