Wavelet-SOM in feature extraction of hyperspectral data for classification of nematode species

A plant´s reflectance can vary significantly depending on the type of stressors affecting it. Parasitic nematode species such as Meloidogyne incognita and Rotylenchulus reniformis are two of the leading nematode species affecting cotton plants. There is a need to detect the type of nematode in order to start proper nematode management program. Use of remotely sensed hyperspectral data could be one of the choices for species identification but, remotely sensed hyperspectral data are usually associated with high dimensions and requires some sort of dimensionality reduction without losing vital information. Some of the standard feature extraction and dimensionality reduction methods widely used nowadays are DWT and Self-Organized Maps (SOM) based methods. In this paper, authors explore the possibility of combining two above mentioned feature extraction and dimensionality reduction methods to extract feature for better classification accuracies pertaining to this study. The accuracies were then compared with the accuracies obtained using features extracted from DWT and SOM-based methods separately. For the entire analysis, SOM-supervised classification method was used.