Abstract
In this study, the performances of random forest (<small>RF</small>), rotation forest (<small>RoF</small>), and canonical correlation forest (<small>CCF</small>) algorithms were compared and analyzed for classification of hyperspectral imagery. For this purpose, the Airborne Visible/Infrared Imaging Spectrometer (<small>AVIRIS</small>) Indian Pine (<small>IP</small>), the Reflective Optics System Imaging Spectrometer University of Pavia, and the AVIRIS Kennedy Space Center (<small>KSC</small>) data sets were used as main data sources. In addition to the confusion matrix–derived accuracy measures (overall accuracy, kappa coefficient, F-scores), the performances of the algorithms were analyzed in detail considering three diversity measures (Q statistics, correlations, and interrater agreements) and a kappa-error diagram. Results showed that the highest classification accuracies (87% for IP, 94% for PU, and 93% for KSC data sets) were achieved with the use of CCF algorithm, and improvements in classification accuracy were statistically significant compared to RF and RoF. Based on the diversity measures and the kappa-error diagram, individual learners in the CCF ensemble were found to be more diverse and accurate.
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