Vegetation Species Identification Using Hyperspectral Imagery

A good similarity measure is very important to ensure accurate classification of vegetation species using hyperspectral remote sensing data. In this study, the effectiveness of the existing similarity measures, spectral angle mapper (SAM), Euclidean distance (ED), and spectral information divergence (SID) was evaluated using data measured by a field spectrometer. To overcome the limitations of the existing measures, a new metric was developed based on the concept of conditional entropy.

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