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.
Highlights
Hyperspectral data is obtained from space and/or satellite platforms
Another important problem for hyperspectral classification is determining a specific threshold value for methods based on the spectral similarity
When spectra are searched in a hyperspectral image, with any spectral similarity based method, whether this species exists or not, if similar spectral species exist in the data, the method automatically matches the other similar species though an adaptive threshold value is used
Summary
Hyperspectral data is obtained from space and/or satellite platforms. In hyperspectral land cover classification, traditionally, reflectance data that is driven by hyperspectral data is used. It is nearly impossible to find a threshold value that can be used for accurate classification of the species which exhibits similar spectral features. When spectra are searched in a hyperspectral image, with any spectral similarity based method, whether this species exists or not, if similar spectral species exist in the data, the method automatically matches the other similar species though an adaptive threshold value is used. In this case, for an accurate classification, one should know about the ground truth at the classification time which is not possible in every case. For a solution to spectral similarity and time-dependency problems in hyperspectral classification, a spectral derivative-based approach is proposed in this study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
