The article entitled: “Capacity and limits of multimodal remote sensing” by Andrea Marinoni, Saloua Chlaily, Mauro Dalla Mura, Jocelyn Chanussot, Christian Jutten, and Paolo Gamba was accepted to EarthVision 2019.


Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this paper, we aim at predicting the maximum information extraction that can be reached when analyzing a given dataset. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing would take place. Thus, it is expected that they can be used for an operational use in terms of image selection and feature extraction in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for under- standing and quantifying physical phenomena. Experimental results show the consistency of our approach.