The classified image has an overall accuracy of 82.8% with a Kappa coefficient of 0.78.
The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7.
A kappa coefficient of 0.83, 0.81 and 0.85, respectively, were also obtained for 1985, 2000 and 2015 LU/LC maps.
A kappa coefficient of 0.83 and overall accuracy of 91.2% were achieved when applying the proposed PGI in the case of Weifang District, Shandong, China.
Experiment results show that our method achieves an overall accuracy of 96.70% and a kappa coefficient of 0.96 in recognizing nine categories of terrain scene point clouds.
The best results were obtained for a SVM classification with level 30 and merge 97, resulting in a Kappa coefficient of 0.80 and error matrix of 85.31%.
The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images.
A total of ~3590 km2 of thaw lakes and ~10130 km2 of DTLBs were mapped, with an overall accuracy of 99.2% and a Kappa coefficient of 0.988.
The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775.
The entire reaction occurred on the membrane within 30 min. Evaluation in clinical samples revealed 85.2% accuracy with a kappa coefficient of 0.69.
Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75.
