Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

翻译：由于卫星上配备的光学遥感仪器无法通过云体观测,云层覆盖对卫星观测任务有重大影响。我们是第一个在云层覆盖不确定性下解决多个灵活 EOS时间安排问题的国家,目标是最大限度地增加全部观测利润。我们采用了机会制约编程模型来初步描述不确定性,然后通过抽样近似法计算云层覆盖不确定性下的观测利润。随后,为大型观测任务提出了一个改进的模拟Annealing法,结合快速插入战略。实验结果显示,在云层覆盖不确定性下改进的模拟Annealing heuristic法超越了多个AEOS列表问题的其他算法,从而验证了拟议算法的效率和效力。