The mean absolute error and root mean squared error are used to measure the difference between the predicted value of the classifier and the actual result and the smaller the value the better the classification performance; so, it is apparent that the KPCA is superior to PCA.
The greater the value of the silhouette width, the better the classification.
The greater the F value is, the greater the distance between two classes is, namely, the greater the difference between two classes is, the better the classification results are.
The higher the resolution, the better the classification performance, but the higher the processing time.
But the more the sequences are conserved the better the overall classification accuracy.
Lastly, we consider the classification strength values.
He went on: "The classification has a value in giving a steer to people, at the very least.
For better understanding of the classification results, confusion matrices of the classification procedures could be plotted.
The area under ROC curve was 0.97 (0.96–0.97) for ER status dichotomous classification that was slightly better than the reported value for automated algorithms of 0.92 (0.90 0.94) (Ali et al., 2013).
In order to better understand the surplus value and limitations of spectral temporal variability metrics for classification purposes, we analyzed four datasets of different temporal depth, using 344 Landsat scenes across four footprints between 2009 and 2012.
