文摘
In the application of spectral reflectance estimation with multispectral imaging, the filters and training samples jointly influence the accuracy of spectral estimation for learning based methods. In this study, by utilizing one kind of genetic algorithm, the selection of optimal filters is implemented with respect to the representative training samples so as to consider the spectral characteristics of filters, imaging system and test samples simultaneously, which also avoids measurement on the detailed spectral characteristics of filters and imaging system. This filter selection method was tested on a filter-wheel based multispectral imaging device along with a set of interference filters. It is verified that a small number of representative training samples are sufficient for selecting the optimal filters, and the filters selected by this method are more effective for spectral estimation than those obtained from the conventional method.
