Investigation on Land Cover Mapping of Large RS Imagery Using Fuzzy Based Maximum Likelihood Classifier

The success of a large number of real-world applications such as mapping, forestry, and change detection depends on the effectiveness with which land cover classes are extracted from Remotely Sensed (RS) imagery. Application of Fuzzy theory in remote sensing has been of great interest in the remote sensing fraternity particularly when the data are inherently Fuzzy. In this paper, a Fuzzy theory based Maximum Likelihood Classifier (MLC) is discussed. The study aims at amplifying the classification accuracy of large heterogeneous multispectral remote sensor data characterized by the overlapping of spectral classes and mixed pixels. Landsat 8 multispectral data of North Canara District was collected from USGS website and is considered for the research. Seven land use land cover classes were identified over the study area. The study also aims at achieving classification results with a confidence level of 95% with \(\pm 4\%\) error margin. The conducted research attains the predicted classification accuracy and proves to be a valuable technique for classification of large heterogeneous RS multispectral imagery.