Derivative Analysis of Hyperspectral Data

Abstract

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 spectral details from spectral data sets. A modular program was created to perform interactive derivative analysis. This module calculated derivatives using either a convolution (Savitzky–Golay) or finite divided difference approximation algorithm. Spectra were smoothed using one of the three built-in smoothing algorithms (Savitzky–Golay smoothing, Kawata–Minami smoothing, and mean-filter smoothing) prior to the derivative computation procedures. Laboratory spectral data were used to test the performance of the implemented derivative analysis module. 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. Issues related to smoothing and spectral deviation caused by the smoothing or derivative computation algorithms were also observed and are discussed. A scaling effect, resulting from the migration of band separations when using the finite divided difference approximation derivative algorithm, can be used to enhance spectral features at the scale of specified sampling interval and remove noise or features smaller than the sampling interval.