The Effects of Haze on the Accuracy of Maximum Likelihood Classification - Asmala, A.; Shaun, Quegan (HIKARI LTD, 2016)
This study aims to investigate the effects of haze on the accuracy of Maximum Likelihood classification. Data containing eleven land covers recorded from Landsat 5 TM satellite were used. Two ways of selecting training pixels were considered which are choosing from the haze-affected and haze-free data. The accuracy of Maximum Likelihood classification was computed based on confusion matrices where the accuracy of the individual classes and the overall accuracy were determined. The result of the study shows that classification accuracies declines with faster rate as visibility gets poorer when using training pixels from clear compared to hazy data.