Hyperspectral NIR image regression part I: calibration and correction

Hyperspectral imaging instruments produce large amounts of raw data. These raw data in A/D converter counts have a number of errors that can be corrected by calibration. The use of multiple Spectralon calibration standards is shown to correct for both spectral and spatial variations. Optimal results are achieved using a two‐step calibration and correction process. A series of full field of view or external calibration standards is used to transform raw data counts to reflectance values. A grayscale series of internal standards embedded within each hyperspectral image is used to compensate for instrument instability. Second‐order regression models based on these multiple standards provide maximum accuracy. The external standards allow for standardization within a hyperspectral image. The internal standards permit instrument standardization or calibration transfer between hyperspectral images.