Classification of emerald based on multispectral image and PCA

Traditionally, the grade discrimination and classifying of bowlders (emeralds) are implemented by using methods based on people's experiences. In our previous works, a method based on NCS(Natural Color System) color system and sRGB color space conversion is employed for a coarse grade classification of emeralds. However, it is well known that the color match of two colors is not a true "match" unless their spectra are the same. Because metameric colors can not be differentiated by a three channel(RGB) camera, a multispectral camera(MSC) is used as image capturing device in this paper. It consists of a trichromatic digital camera and a set of wide-band filters. The spectra are obtained by measuring a series of natural bowlders(emeralds) samples. Principal component analysis(PCA) method is employed to get some spectral eigenvectors. During the fine classification, the color difference and RMS of spectrum difference between estimated and original spectra are used as criterion. It has been shown that 6 eigenvectors are enough to reconstruct reflection spectra of the testing samples.