Satellite imagery is now widely used in the defense sector for monitoring locations of interest. Although the increasing amount of data enables pattern identification and therefore prediction, carrying this task manually is hardly feasible. We hereby propose a cased-based reasoning approach for automatic prediction of rare events on strategic sites. This method allows direct incorporation of expert knowledge, and is adapted to irregular time series and small-size datasets. Experiments are carried out on two use-cases using real satellite images: the prediction of submarines arrivals and departures from a naval base, and the forecasting of imminent rocket launches on two space bases. The proposed method significantly outperforms a random selection of reference cases on these challenging applications, showing its strong potential.
翻译:卫星图象现在被广泛用于国防部门,用于监测感兴趣的地点。虽然越来越多的数据能够进行模式识别,从而进行预测,但人工执行这项任务几乎不可行。我们在此提出一种基于案例的推理方法,用于在战略地点自动预测稀有事件。这种方法可以直接纳入专家知识,并适应不规则的时间序列和小型数据集。利用实际卫星图像对两个使用案例进行了实验:预测潜艇抵达和离开海军基地,以及预测即将在两个空间基地发射的火箭。拟议方法大大优于随机选择关于这些具有挑战性的应用的参考案例,显示了其强大的潜力。