Quantum Fusion Intelligence for Integrated Satellite-Ground Remote Sensing

Satellite imagery plays a crucial role in integrated satellite-ground remote sensing (SGRS), particularly in applications such as disaster management and military intelligence, where real-time monitoring and forecasting are essential for effective decision-making. However, narrow artificial intelligence (AI) models often face challenges in processing large-scale high-dimensional data efficiently while maintaining the required accuracy and speed, limiting their effectiveness in time-sensitive scenarios. To address these challenges, we explore the integration of satellite remote sensing with large AI, quantum computing, and quantum communication technologies, focusing on enhancing computational efficiency and data security in integrated SGRS systems. Specifically, we put forth an integrated quantum SGRS framework, which combines quantum fusion intelligence (QFI) with quantum anonymous communication (QAC). By integrating quantum and large AI, the QFI models enhance the efficiency, accuracy, and security of satellite imagery analysis while ensuring that the extracted information is transmitted to ground stations in a privacy-preserving manner using QAC. This approach is particularly effective in time- and privacy-sensitive scenarios. To demonstrate the effectiveness of QFI computing, we present case studies in disaster detection and environmental monitoring. This research highlights the transformative potential of quantum-large AI integration in SGRS and its implications for nonterrestrial-terrestrial quantum networks.