High-Level Information Fusion in a Mission Planning Context

Observation missions often consist of deploying aircrafts, satellites, vessels and ground vehicles to collect imagery of areas of interest. The cost of deploying these platforms to perform missions is enormous. Mission planning is considered to be a precursor to observation missions, it is used to attest their usefulness and optimize them. Image quality prediction is crucial for mission planning. It has the benefit to allow organizations to optimize observation missions so that the collected imagery of missions are of good quality. The most popular method for predicting the quality of imagery is the General Image Quality Equation. This paper presents various methods, each attached to a particular context of observation missions, in order to predict image quality. The presented methods will focus on the use of Artificial Intelligence to predict the quality of images. Research in the sensor fusion field has proven that an image resulting from the fusion of two images from different sensors can provide more information than individual images. Several articles on image quality estimation do not consider the benefit of sensor fusion during mission planning. Therefore, the main innovation of this work will be the prediction of the quality of fused images. The results obtained during the experiments show the effectiveness of the proposed methods.