Decision support system for the selection of classification methods for remote sensing imagery

It is generally recognized that classification methods for remote sensing imagery show different performance levels depending on the type of dataset or classification scheme. Moreover, factors such as spectral bands, the use of ancillary data, and study area characteristics have an effect. Consequently, when users attempt land-cover classification, they encounter difficulties in selecting an appropriate image classification method. This study suggests a model Decision Support System (DSS) to assist in the selection of the optimal classification method or scheme. The DSS is established on case-based reasoning, which helps users to figure out solutions for new situations by comparison with successful experiences in the past. In this study, a hybrid method incorporating a feature-based and hierarchical structure is used to construct a case base. Four key features, dataset, location, climate, and class, are used. The DSS suggested in this study offers a graphical user interface that allows users to choose various conditions on-screen in a hierarchical structure.