Spatial data mining model for satellite image classifier

Spatial data mining, mining knowledge from large amounts of spatial data, is a highly demanding field. Huge amounts of spatial data have been collected in various applications, i.e. remote sensing, geographical information systems, environmental assessment and planning. The collected data exceeded human ability to analyze. This thesis shows a proposed data-mining model for mining a spatial data acquitted from a remote sensed satellite images.; The thesis is divided into six chapters. Chapter one gives an overview of spatial data mining concept and includes problem definition and research objectives. Chapter two explores the theoretical framework of spatial data mining and explains the spatial data-mining phases. Chapter three centers on an unsupervised classification technique, as an essential part of the proposed spatial data-mining model. Chapter four illustrates the proposed model with used algorithms explanation, complexity and implementation. Chapter five shows the selected dataset, applied experiments, and the results.; Finally, chapter six is the conclusion, which includes summary of work, future recommendation and limitations.