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Regional monitoring of forest vegetation using airborne hyperspectral remote sensing data

Some results are given of the airborne applications to recognize forest classes of different species and ages for a test area based on the imaging spectrometer produced in Russia. Optimization techniques are outlined to select the most informative spectral bands for the particular subject area of the forest applications using the improved Bayesian classifier in the pattern recognition supervising procedures. A successive addition method is used in this optimization with the calculation of the probability error of the statistical pattern recognition while collecting the spectral ensembles for the known classes of forest vegetation for different species and ages. The subsequent step up method consists in fixing the level of the probability error that is not improved by adding the channels in the related computational procedures. The best distinguishable classes are recognized at the first stage of these procedures. The analytical technique called “cross-validation” is used for this purpose. The second stage is realized as a stable feature selection method based on the standard stepwise optimization approach, holdout cross-validation and resampling.