Comparison of the efficacy of spectral pre-treatments for wheat and weed discrimination in outdoor conditions

• We modeled spectral variability obtained with wheat and weed reflectance spectra.
• We examined pre-treatment influence on discrimination results using PLS-LDA and SVM.
• We discussed the bias and class separability obtained with PLS-LDA scores.
• Results prove that smoothing and logarithm are the best pre-treatments for this study.

The optimal processing of spectral data often requires specific pre-treatments. In the context of spectral discrimination, results can be greatly improved using the relevant pre-treatment. Most importantly, the pre-treatment must be suited to the nuisance variability that has to be removed. This study focuses on discrimination of weed and wheat using spectra acquired in outdoor conditions with uncontrolled lighting and leaf orientations. Both vegetation spectra are highly similar, and due to the context of acquisition, a lot of spectral variability is present. This nuisance variability is modeled using an additive, multiplicative and noise term, each of which affects the measured spectra. Several pre-treatments were therefore evaluated according to their potential to deal with this variability and their effects were described in the feature space. Finally, results obtained with these pre-treatments combined with two discrimination methods (PLS-LDA and Gaussian SVM) are compared and discussed. Results showed that, thanks to their ability to remove nuisance variability, most pre-treatments are effective in terms of classification accuracy. Gaussian SVM classification results are less influenced by pre-treatments than those of PLS-LDA, since the former compensates the pre-treatment effect by using a different non-linear kernel. For this data-set, the best discrimination result was obtained using the combination logarithm and PLS-LDA. Logarithm actually transforms the multiplicative effect into and additive one, which is then effectively dealt with by PLS-LDA.