Derivative analysis of hyperspectral data

With the goal of applying derivative spectral analysis to analyze high resolution, spectrally continuous remote sensing data, several smoothing and derivative computation algorithms have been reviewed and modified to develop a set of cross-platform spectral analysis tools. Emphasis was placed on exploring different smoothing and derivative algorithms to extract subtle spectral features from any continuous spectral data sets. With interactive selection of bandwidth and sampling interval (band separation), the algorithm can optimize noise reduction and better match the scale of spectral features of interest. Laboratory spectral data were used to test the performance of the implemented derivative analysis modules. An algorithm for detecting the absorption band positions was executed on synthetic spectra and a soybean fluorescence spectrum to demonstrate the usage of the implemented modules in extracting spectral features. Upon examination of the developed modules, issues related to the smoothing and the spectral deviation caused by the smoothing or derivative computation algorithms were also observed and discussed. The scaling effect resulting from the migration of band separations when using the finite approximation derivative algorithm was thoroughly inspected to understand the relationship between the scaling effect and noise removal.