Selecting an appropriate classifica

Selecting an appropriate classification algorithm to classify the imagery can improve the overall accuracy, as the quality of classification results directly affect the performance of the change detection (Wang et al., 2015). Therefore, determining an appropriate classifier is significant. In this study, supervised classification methods and machine learning algorithms, including MLC, SVM, ANN and DT, were performed on both the 2006 and 2015 mosaicked images. Training samples were selected for the above six categories based on false colour composite of the reflective spectral bands. Based on the Jeffries Matusita (JM) distance report, which ranges from 0 to 2 and indicates an average distance between a pair of classes contributing to how accurate classification results will be, it can be therefore used to detect the spectral separability of training samples (Schmidt et al., 2003). If the value is asymptotic to 0, the selected training samples are more polymerized