Multisensor raster and vector data fusion based on uncertainty modeling

We propose a new methodology for fusing temporally changing multisensor raster and vector data by developing a spatially and temporally varying uncertainty model of acquired and transformed multisensor measurements. The proposed uncertainty model includes errors due to (1) each sensor by itself, e.g., sensor noise; (2) transformations of measured values to obtain comparable physical entities for data fusion and/or to calibrate sensor measurements; (3) vector data spatial interpolation that is needed to match different spatial resolutions of multisensor data; and (4) temporal interpolation that has to take place if multisensor acquisitions are not accurately synchronized. The proposed methodology was tested using simulated data with varying (a) amount of sensor noise, (b) spatial offset of point sensors generating vector data, and (c) model complexity of the underlying physical phenomenon. We demonstrated the multisensor fusion approach with a data set from a structural health monitoring application domain.