Calibrating consists in predicting Y, a quantitative variable of interest, using P explaining variables. PLSR-Projection to Latent Structures Regression (Wold,[1]) is the most popular method, very powerfull when a calibration dataset is available. Other methods don’t need a calibration dataset, they are called direct calibrations. Two of them have been proposed previously: DC-Direct Calibration (as described in Martens and Naes, [2]) and SBC-Science Based Calibration (Marbach([3])). New method called IDC-Improved Direct Calibration is proposed. As for DC, this approach is based on an orthogonal projection. IDC projector is obtained by merging DC projector (consisting only in pure spectra of chemical compounds), and vectors characterising physical influence factors (consisting in PCA loadings onto a design dataset). Indeed, hyperspectral image analysis is a case where calibration data are not available. Thus, it’s interesting to use direct calibration methods instead of PLSR. With the prior knowledge of a few reference spectra, and modelling some noise from the hyperspectral image itself, it’s possible to identify objects of interest from the background. This method is also simple and understandable, very quick and easy to compute.