Classification of remote sensed data using linear kernel based support vector machines

Study of remote sensed imagery has gained practical significance in various domains such as environmental monitoring, fire risk mapping, change detections and land use. Classification is a data mining methodology which is used to assign class labels to data instances and build a model so as to be able to predict class labels for unlabelled data. In this paper algorithms based on parametric distribution model like k nearest neighbor classifier and linear kernel based support vector machines classifier are used for classifying remote sensed data. A generic algorithm is discussed to implement the said classification. We finally analyze the performance of these algorithms based on various parameters.