An alternative paradigm for data evaluation in remote sensing using multisensor data fusion

In remote sensing, image data are evaluated according to different concepts. From a scientific point of view the goal of the evaluation often is to extract as much information as possible from a given data set. For practical applications the goal is rather to obtain information as efficiently as possible and as reliably as necessary. Starting with these observations the paper discusses and contrasts two paradigms for data evaluation: the first one aiming at enhanced data evaluation, and the second one with the goal of effective information extraction. It argues that under the second paradigm multisensor data fusion is very advantageous. In the near future, an increasing amount of multisensor data will be provided by satellite-borne as well as airborne platforms. As at the same time practical applications of remote sensing will become more widespread than in the past, the second paradigm for data evaluation is increasingly important. Consequently, there will be an increasing need for approaches and algorithms for data fusion