Combining image derived spectra and physics based models for hyperspectral image exploitation

This paper addresses a conceptual approach to hyperspectral image assessment that uses physics-based models to constrain multiparameter inversion algorithms aimed at quantitative measurement of material properties. Sensing approaches include use of a single hyperspectral image with many spectral samples, a sequence of images acquired over time, and widely different types of measurements from a range of sensor types. We propose a conceptual approach for merging these data into a common framework to allow simultaneous exploitation of these multiparameter data sets. Physics-based models predict or constrain the range of observable parameters associated with a target or material condition, then model matching or optimization methods invert a measurement set to a target type or condition. 2 examples are presented using hyperspectral data sets. The first involves characterizing atmospheric constituents using the MODTRAN Code to constrain the solutions. The second uses a radiation propagation model to drive an inversion of hyperspectral data to multiple water quality parameters. Finally, we discuss how more involved 3D physics-based synthetic image models may hold a key to image exploitation algorithms