Data Analysis - Processing Requirements and Available Software Tools
Methods for analysing remote sensing data are conditioned by sensor technology as much as by the particular application. However, in the past twenty years a number of techniques have proved to be widely applicable and have become standard tools available in public domain or commercially offered software packages. Many of these tools are no longer applicable to data acquired with imaging spectrometers either because of the high dimensionality of the data which increases processing time beyond practical bounds (e. g. maximum likelihood classifiers), or because of the high statistical variability of data which again is directly linked to the data space dimension (e. g. clustering algorithms), or simply because implementations have arbitrary limits for the processable number of bands. New techniques have evolved which exploit the continuity of spectral information available with imaging spectrometers (e. g. correlation and fitting algorithms) or the detail observed in the short wave infrared region. Also techniques which have not been generally accepted in the past have been reconsidered within the context of image spectrometry (e. g. spectral unmixing).
KeywordsDigital Elevation Model Spectral Band Atmospheric Correction Imaging Spectrometer Maximum Likelihood Classifier
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