Ensemble-based canonical correlation forest (CCF) for land use and land cover classification using sentinel-2 and Landsat OLI imagery

Classifying spectrally similar crop types in fragmented landscapes is a difficult task due to the low spectral and spatial resolution of satellite imagery. The objective of this study is twofold: (I) to evaluate the performance of a recent ensemble methodology, namely canonical correlation forest (CCF), and (ii) to investigate the potential of recently launched Sentinel-2 (S-2) image for crop classification. The algorithmis based on building a forest-type ensemble model using multiple canonical correlation trees. Its performance was compared to widely-used random forest (RF) and rotation forest (RotFor) for the classification of 4-band and 10-band S-2 and Landsat-8 (L-8) OLI datasets. In addition to overall accuracy estimations, results of the methods were assessed using recently introduced map-level and category-level disagreement measures. Results showed that the CCF and RotFor algorithms produced statistically similar results for the S-2 datasets but statistically different results for the OLI image whilst outperforming the RF algorithm for all cases. The CCF algorithm was found less sensitive to the ensemble size (i.e. number of trees) compared to RF algorithm. Also, for the S-2 data inclusion of six spectral bands at 20 m resolution resulted in a significant increase in classification accuracy (up to 6%).