Fig. 8

Receiver operating characteric curves and dependence of classification accuracy on selected thresholds. ROC curves visualize the trade-off between successful detection of healthy versus infected and severely diseased (ROC-1), infected and severely diseased versus healthy (ROC-2), and severely diseased versus all other bunches (ROC-3) and the corresponding error rates. ROC curves are calculated for the complete dataset of 60 images. Class decision for each bunch is based on the average fraction of diseased pixels of two images (top and bottom view of the bunch). This combined score was calculated for each bunch prior to application of a threshold. The top row shows the results for classification based on spectral features, while the bottom row shows the results for spatial-spectral features with Random Forest classifiers (50 trees each). A true positive rate of 1 means that all bunches of the corresponding class have been successfully assigned to the correct class. This is achieved at the price of a certain false positive rate, which denotes the fraction of bunches of the other classes falsely assigned to the same class. ROC-1 and ROC-2 are significantly improved by using spatial-spectral features. As two thresholds are needed to separate the 3 classes, the last column visualizes the accuracy as a function of the selected thresholds A and B. The optimal combination of thresholds is highlighted for both feature spaces and shows a significant gain in overall classification accuracy for our spatial-spectral approach