A Hierarchical Classificaton of Landsat Tm Imagery for Landcover Mapping

Information about current land-cover in forests is important for management and conservation of these areas. Up to the last decade traditional per pixel classification algorithms were used to be utilized in extracting land-cover information. However, they are poorly equipped to monitor land-cover in images acquired by current generation of satellite sensors with adequate accuracy. A good understanding and classification of an image can be done by gathering critical a priory knowledge about the study area and an effective use of channels involved in the procedure. It is important to make use additional spectral and spatial knowledge in order to improve the classification accuracy. In this study, a knowledge based hierarchical approach is proposed in order to classify and detect forest types in the Ömerli Dam Lake Region. The method makes use of the fact that land-cover types and their associated knowledge form a natural hierarchy. Hierarchical classification is a powerful approach in solving classification problems by decomposing the image into a hierarchical tree structure. This also results in sub-dividing the area into spectrally consistent regions and helps dealing with spectral variability within each subarea. Three types of knowledge were involved in the rule-based classification of the study area: Domain spectral knowledge, Spectral classification rules obtained from training data and Spatial knowledge. Sub-dividing the area into smaller homogeneous regions in hierarchical classification increased the accuracy, while supervised classification technique yielded 47 per cent in the same area. Spatial reclassification involved in the hierarchical classification method increased overall accuracy, yielding new classes like coast.