ACRS2015_4.pdf

ABSTRACT: The main objective of this research is to apply a refined Mahalanobis distance classifier for the extraction of forest class information from the combined optical and microwave images. The refined classification method uses spatial thresholds defined from contextual knowledge and different features obtained through a feature extraction process. The result of the refined method is compared with the results of a standard classification method and it demonstrates a higher accuracy. Overall, the research indicates that multisource data can improve the classification of forest types and the elaborated refined classification method is a powerful tool to increase the classification accuracy.