A novel derivative-based classification method for hyperspectral data processing

Abstract

In hyperspectral classification, a derivative of reflectance spectra is used directly or by fusion with the reflectance spectra. In this way, classification performance is improved. However, on the land cover, especially for plant species, the reflectance spectra may exhibit differences depending on a plant age and maturity level. This situation makes traditional classification methods which are based on time-dependent spectral similarity. In addition, the problem of classification of the species which have similar spectral properties is still valid. As a solution to time dependency and spectral similarity problems, in this study, a new and more generic method based on the spectral derivative is proposed. The method is tested for hyperspectral images which are captured at different time of the year and different places, in the life cycle of species. Test results show that proposed method successfully classifies the land cover time-independent and it is superior to the classical classification methods.