Spectral Quality Equation Relating Collection Parameters to Material Identification Performance

摘要

A methodology capable of quantitatively assessing the quality of hyperspectral data has become increasingly desirable as hyperspectral remote sensing technology migrates into operational systems. The quality of spectral data depends on many factors including collection parameters charactering the sensor and the scene, and the desired spectral products. Therefore, there is a recognized urgent need to understand the phenomenology associated with the collection parameters and how they relate to the quality of the information extracted from the spectral data for different applications. If such relationships can be established, data collection requirements and tasking strategies can then be formulated for these applications. A spectral quality equation with an excellent least-squares fit was established for object/anomaly detection in an earlier work. This paper describes a spectral quality equation established for material identification. This spectral quality equation relates the collection parameters (i.e. spatial resolution, spectral resolution, signal-to-noise ratio, and scene complexity) to the probability of correct identification (Pi) of materials at a given probability of false alarms (Pfa).