DERIVATIVE ANALYSIS OF HYPERSPECTRAL DATA

With the goal of applying derivative spectral analysis to analyze high -resolution, spectrally continuous remote sensing data, several smooth ing and derivative computation algorithms have been reviewed and modif ied to develop a set of cross-platform spectral analysis tools. Emphas is was placed on exploring different smoothing and derivative algorith ms to extract spectral details from spectral data sets. A modular prog ram was created to perform interactive derivative analysis. This modul e calculated derivatives using either a convolution (Savitzky-Golay) o r finite divided difference approximation algorithm. Spectra were smoo thed using one of the three built-in smoothing algorithms (Savitzky-Go lay smoothing, Kawata-Minami smoothing, and mean-filter smoothing) pri or to the derivative computation procedures. Laboratory spectral data were used to test the performance of the implemented derivative analys is module. An algorithm for detecting the absorption band positions wa s executed on synthetic spectra and a soybean fluorescence spectrum to demonstrate the usage of the implemented modules in extracting spectr al features. Issues related to smoothing and spectral deviation caused by the smoothing and spectral deviation caused by the smoothing or de rivative computation algorithms were also observed and are discussed. A scaling effect, resulting from the migration of band separations whe n using the finite divided difference approximation derivative algorit hm, can be used to enhance spectral features at the scale of specified sampling interval and remove noise or features smaller than the sampl ing interval. (C)Elsevier Science Inc., 1998.