A new methodology for the discrimination of plant species and their varieties using hyperspectral data: application on vetch and lentil

This paper presents a new methodology for the discrimination of plant species and their varieties using hyperspectral data. The concept lies on the combination of spectral pre-processing algorithms (SPPA) that enhance spectral discrimination between species and their varieties. SPPA use as input a single spectral signature and transform it according to the SPPA function. A k-step combination of SPPA uses k pre-processing algorithms serially. Initially each spectral signature is used as input to the first SPPA. The result of this SPPA is used as input to the second SPPA, and so on until the desired pre-processed signatures are reached. These signatures are then discriminated by applying spectral matching algorithms. The performance of the combination is evaluated based on the number of correctly matched signatures. In this work a k-step combination of SPPA has been set up, with k ranging from 1 to 3. The following SPPA have been investigated: vector normalization, Fourier transformation, Logarithm transformation, Kubelka-Munck transformation, derivatives, continuum removal, band depth, value normalization, n order square root transformation, and smoothing. There is a very large number of possible combinations of the aforementioned SPPAs, thus a Simple Genetic Algorithm has been used for finding optimum combinations. The input hyperspectral data were the spectral signatures of 9 varieties of vetches and 9 varieties of lentils, measured by the GER1500 spectroradiometer. For all the samples, the spectral signatures were measured at two slightly different times in the growing season. The results showed that several combinations exist which can successfully discriminate and label the spectral signatures in terms of variety, and they are independent from the time of the spectral signature measurement.