Compressive data fusion for multi-sensor image analysis

Multiple views of a scene - obtained via different sensing modalities - have the potential to significantly enhance image analysis for remote sensing and other applications. This benefit is expected to be significant if the multiple views are providing independent, yet useful, information about the underlying classes in a scene. To exploit such multi-sensor information, a compressive-projection approach to the fusion of multi-sensor imagery is proposed. It is argued that that random projections yield subspaces that preserve the discriminative nature of multi-sensor datasets with profound implications in a practical scenario wherein compressive measurements can directly facilitate data fusion without the need for complicated subspace-learning approaches. A case study fusing experimental hyperspectral and LiDAR data demonstrates that statistical learning in the compressive-measurement domain is not only feasible, but also provides a natural framework for sensor fusion without the need for explicit reconstruction from compressive measurements.