Investigation on Land Cover Mapping Capability of Maximum Likelihood Classifier: A Case Study on North Canara, India

Abstract

The success of a large number of real-world applications depends on the efficiency with which land use land cover classes are extracted from Remotely Sensed (RS) imagery. This study aims at analysing the performance of Maximum Likelihood Classifier (MLC) over multispectral remote sensor data characterized by the overlapping of spectral classes and presence of mixed pixels, investigating the impact of prior class probabilities on the overall classification performance, the impact of the number of land cover classes on classification accuracy, and identifying the decision parameters for assigning a pixel to a land cover class. A quantitative analysis of decision parameters of MLC for pixel assignment is presented for five randomly selected pixels in the study area. 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. Results obtained indicate that use of prior probability estimates improves the classification accuracy. The study also presents the variation in classification accuracy for four different cases of total number of land cover classes. The conducted research is useful in understanding the performance of MLC under multiple scenarios.