Hyperspectral Image Classification of Grass Species in Northeast Japan

This paper investigates the application of artificial neural networks for classifying grass species from hyperspectral image data. High-resolution spatial and spectral data of localized fields were collected using a hyperspectral sensor mounted on the tip of a crane. The hyperspectral datasets are processed using normalization and second derivative in order to reduce the effect of variations in the intensity level of reflectance and to improve the classification accuracy and generalization performance of the neural network-based model. An experimental comparison of the pre-processing methods shows that the best classification accuracy is obtained by the second derivative transformed dataset. Normalization, and a combination of both methods, did not improve accuracy of the neural network models of our experimental datasets more than simple raw reflectance.