An Evaluation of Popular Hyperspectral Images Classification Approaches

This work is devoted to the problem of the best hyperspectral images classification algorithm selection. The following algorithms are used for comparison: decision tree using full cross-validation; decision tree C 4.5; Bayesian classifier; maximum-likelihood method; MSE minimization classifier, including a special case - classification by conjugation; spectral angle classifier (for empirical mean and nearest neighbor), spectral mismatch classifier and support vector machine (SVM). There are used AVIRIS and SpecTIR hyperspectral images to conduct experiments.