From Hyperspectral to Multispectral Sensing and from Simulation to Reality: A Comprehensive Approach to Calibration Model Transfer

High-resolution hyperspectral sensors provide precise but expensive information on an object’s chemical composition in various industries. We present a method for transferring this capability to customized low-cost multispectral solutions. Taking a relevance analysis of spectra for a given problem as our starting point, we simulated and designed a multispectral sensor based on inverse spectroscopy. The corresponding calibration model, which was derived from the simulation of such a multispectral sensor and connected with its hardware, may not drop in precision significantly. Different methods of calibration model transfer capable of handling a limited subset of the data were tested for this purpose. The latent space transformation with Chebyshev polynomials outperformed all other methods by yielding the fewest labeled data.